2016
|
Alessia Saggese; Nicola Strisciuglio; Mario Vento; Nicolai Petkov Time-frequency analysis for audio event detection in real scenarios Inproceedings 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 438-443, 2016. Abstract | BibTeX | Tag: | Links: @inproceedings{7738082,
title = {Time-frequency analysis for audio event detection in real scenarios},
author = {Alessia Saggese and Nicola Strisciuglio and Mario Vento and Nicolai Petkov},
doi = {10.1109/AVSS.2016.7738082},
year = {2016},
date = {2016-08-01},
booktitle = {2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
pages = {438-443},
abstract = {We propose a sound analysis system for the detection of audio events in surveillance applications. The method that we propose combines short- and long-time analysis in order to increase the reliability of the detection. The basic idea is that a sound is composed of small, atomic audio units and some of them are distinctive of a particular class of sounds. Similarly to the words in a text, we count the occurrence of audio units for the construction of a feature vector that describes a given time interval. A classifier is then used to learn which audio units are distinctive for the different classes of sound. We compare the performance of different sets of short-time features by carrying out experiments on the MIVIA audio event data set. We study the performance and the stability of the proposed system when it is employed in live scenarios, so as to characterize its expected behavior when used in real applications.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a sound analysis system for the detection of audio events in surveillance applications. The method that we propose combines short- and long-time analysis in order to increase the reliability of the detection. The basic idea is that a sound is composed of small, atomic audio units and some of them are distinctive of a particular class of sounds. Similarly to the words in a text, we count the occurrence of audio units for the construction of a feature vector that describes a given time interval. A classifier is then used to learn which audio units are distinctive for the different classes of sound. We compare the performance of different sets of short-time features by carrying out experiments on the MIVIA audio event data set. We study the performance and the stability of the proposed system when it is employed in live scenarios, so as to characterize its expected behavior when used in real applications. |
Luca Greco; Pierluigi Ritrovato; Alessia Saggese; Mario Vento Improving reliability of people tracking by adding semantic reasoning Inproceedings 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 194-199, 2016. Abstract | BibTeX | Tag: | Links: @inproceedings{7738025,
title = {Improving reliability of people tracking by adding semantic reasoning},
author = {Luca Greco and Pierluigi Ritrovato and Alessia Saggese and Mario Vento},
doi = {10.1109/AVSS.2016.7738025},
year = {2016},
date = {2016-08-01},
booktitle = {2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
pages = {194-199},
abstract = {Even the best performing object tracking algorithm on well known datasets, commits several errors that prevent a concrete adoption in real case scenarios unless you do not accept some compromise about tracking quality and reliability. The aim of this paper is to demonstrate that adding to a traditional object tracking solution a knowledge based reasoner build on top of semantic web technologies, it is possible to identify and properly manage common tracking problems. The proposed approach has been evaluated using View 001 and View 003 of the PETS2009 dataset with interesting results.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Even the best performing object tracking algorithm on well known datasets, commits several errors that prevent a concrete adoption in real case scenarios unless you do not accept some compromise about tracking quality and reliability. The aim of this paper is to demonstrate that adding to a traditional object tracking solution a knowledge based reasoner build on top of semantic web technologies, it is possible to identify and properly manage common tracking problems. The proposed approach has been evaluated using View 001 and View 003 of the PETS2009 dataset with interesting results. |
Pasquale Foggia; Nicolai Petkov; Alessia Saggese; Nicola Strisciuglio; Mario Vento Audio Surveillance of Roads: A System for Detecting Anomalous Sounds Journal Article IEEE Transactions on Intelligent Transportation Systems, 17 (1), pp. 279-288, 2016, ISSN: 1524-9050. Abstract | BibTeX | Tag: | Links: @article{7321013,
title = {Audio Surveillance of Roads: A System for Detecting Anomalous Sounds},
author = {Pasquale Foggia and Nicolai Petkov and Alessia Saggese and Nicola Strisciuglio and Mario Vento},
doi = {10.1109/TITS.2015.2470216},
issn = {1524-9050},
year = {2016},
date = {2016-01-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {17},
number = {1},
pages = {279-288},
abstract = {In the last decades, several systems based on video analysis have been proposed for automatically detecting accidents on roads to ensure a quick intervention of emergency teams. However, in some situations, the visual information is not sufficient or sufficiently reliable, whereas the use of microphones and audio event detectors can significantly improve the overall reliability of surveillance systems. In this paper, we propose a novel method for detecting road accidents by analyzing audio streams to identify hazardous situations such as tire skidding and car crashes. Our method is based on a two-layer representation of an audio stream: at a low level, the system extracts a set of features that is able to capture the discriminant properties of the events of interest, and at a high level, a representation based on a bag-of-words approach is then exploited in order to detect both short and sustained events. The deployment architecture for using the system in real environments is discussed, together with an experimental analysis carried out on a data set made publicly available for benchmarking purposes. The obtained results confirm the effectiveness of the proposed approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In the last decades, several systems based on video analysis have been proposed for automatically detecting accidents on roads to ensure a quick intervention of emergency teams. However, in some situations, the visual information is not sufficient or sufficiently reliable, whereas the use of microphones and audio event detectors can significantly improve the overall reliability of surveillance systems. In this paper, we propose a novel method for detecting road accidents by analyzing audio streams to identify hazardous situations such as tire skidding and car crashes. Our method is based on a two-layer representation of an audio stream: at a low level, the system extracts a set of features that is able to capture the discriminant properties of the events of interest, and at a high level, a representation based on a bag-of-words approach is then exploited in order to detect both short and sustained events. The deployment architecture for using the system in real environments is discussed, together with an experimental analysis carried out on a data set made publicly available for benchmarking purposes. The obtained results confirm the effectiveness of the proposed approach. |
Carmine Sansone; Daniel Pucher; Nicole M. Artner; Walter G. Kropatsch; Alessia Saggese; Mario Vento Shape Normalizing and Tracking Dancing Worms Book Chapter Robles-Kelly, Antonio; Loog, Marco; Biggio, Battista; Escolano, Francisco; Wilson, Richard (Ed.): Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, S+SSPR 2016, M'erida, Mexico, November 29 - December 2, 2016, Proceedings, pp. 390–400, Springer International Publishing, Cham, 2016, ISBN: 978-3-319-49055-7. Abstract | BibTeX | Tag: | Links: @inbook{Sansone2016,
title = {Shape Normalizing and Tracking Dancing Worms},
author = {Carmine Sansone and Daniel Pucher and Nicole M. Artner and Walter G. Kropatsch and Alessia Saggese and Mario Vento},
editor = {Antonio Robles-Kelly and Marco Loog and Battista Biggio and Francisco Escolano and Richard Wilson},
url = {http://dx.doi.org/10.1007/978-3-319-49055-7_35},
doi = {10.1007/978-3-319-49055-7_35},
isbn = {978-3-319-49055-7},
year = {2016},
date = {2016-01-01},
booktitle = {Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, S+SSPR 2016, M'erida, Mexico, November 29 - December 2, 2016, Proceedings},
pages = {390--400},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {During spawning, the marine worms Platynereis dumerilii exhibit certain swimming behaviors, which are described as nuptial dance. To address the hypothesis that characteristic male and female spawning behaviors are required for successful spawning and fertilization, we propose a 2D tracking approach enabling the extraction of spatio-temporal data to quantify gender-specific behaviors. One of the main issues is the complex interaction between the worms leading to collisions, occlusions, and interruptions of their continuous trajectories. To maintain the individual identities under these challenging interactions a combined tracking and re-identification approach is proposed. The re-identification is based on a set of features, which take into account position, shape and appearance of the worms. These features include the normalized shape of a worm, which is computed using a novel approach based on its distance transform and skeleton.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
During spawning, the marine worms Platynereis dumerilii exhibit certain swimming behaviors, which are described as nuptial dance. To address the hypothesis that characteristic male and female spawning behaviors are required for successful spawning and fertilization, we propose a 2D tracking approach enabling the extraction of spatio-temporal data to quantify gender-specific behaviors. One of the main issues is the complex interaction between the worms leading to collisions, occlusions, and interruptions of their continuous trajectories. To maintain the individual identities under these challenging interactions a combined tracking and re-identification approach is proposed. The re-identification is based on a set of features, which take into account position, shape and appearance of the worms. These features include the normalized shape of a worm, which is computed using a novel approach based on its distance transform and skeleton. |
Peter Hobson; Brian C. Lovell; Gennaro Percannella; Alessia Saggese; Mario Vento; Arnold Wiliem Computer Aided Diagnosis for Anti-Nuclear Antibodies HEp-2 images: Progress and challenges Journal Article Pattern Recognition Letters, 82, Part 1 , pp. 3 - 11, 2016, ISSN: 0167-8655, (Pattern recognition Techniques for Indirect Immunofluorescence Images Analysis). Abstract | BibTeX | Tag: Computer Aided Diagnoses | Links: @article{Hobson20163,
title = {Computer Aided Diagnosis for Anti-Nuclear Antibodies HEp-2 images: Progress and challenges},
author = {Peter Hobson and Brian C. Lovell and Gennaro Percannella and Alessia Saggese and Mario Vento and Arnold Wiliem},
url = {http://www.sciencedirect.com/science/article/pii/S0167865516301374},
doi = {http://dx.doi.org/10.1016/j.patrec.2016.06.013},
issn = {0167-8655},
year = {2016},
date = {2016-01-01},
journal = {Pattern Recognition Letters},
volume = {82, Part 1},
pages = {3 - 11},
abstract = {The Anti-Nuclear Antibodies (ANA) test using Human Epithelial (HEp-2) cells has been the gold standard to identify the presence of Connective Tissue Diseases (CTD) such us Systemic Lupus Erythomatus (SLE). As the ANA test is time consuming, labor intensive and subjective, there has been an on-going effort to develop image-based Computer Aided Diagnosis (CAD) systems. This paper discusses the current progress and challenges in this field and will highlight areas which require more attention.},
note = {Pattern recognition Techniques for Indirect Immunofluorescence Images Analysis},
keywords = {Computer Aided Diagnoses},
pubstate = {published},
tppubtype = {article}
}
The Anti-Nuclear Antibodies (ANA) test using Human Epithelial (HEp-2) cells has been the gold standard to identify the presence of Connective Tissue Diseases (CTD) such us Systemic Lupus Erythomatus (SLE). As the ANA test is time consuming, labor intensive and subjective, there has been an on-going effort to develop image-based Computer Aided Diagnosis (CAD) systems. This paper discusses the current progress and challenges in this field and will highlight areas which require more attention. |
Peter Hobson; Brian C. Lovell; Gennaro Percannella; Alessia Saggese; Mario Vento; Arnold Wiliem HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results Journal Article Pattern Recognition Letters, 82, Part 1 , pp. 12 - 22, 2016, ISSN: 0167-8655, (Pattern recognition Techniques for Indirect Immunofluorescence Images Analysis). Abstract | BibTeX | Tag: Computer Aided Diagnoses | Links: @article{Hobson201612,
title = {HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results},
author = {Peter Hobson and Brian C. Lovell and Gennaro Percannella and Alessia Saggese and Mario Vento and Arnold Wiliem},
url = {http://www.sciencedirect.com/science/article/pii/S0167865516301751},
doi = {http://dx.doi.org/10.1016/j.patrec.2016.07.013},
issn = {0167-8655},
year = {2016},
date = {2016-01-01},
journal = {Pattern Recognition Letters},
volume = {82, Part 1},
pages = {12 - 22},
abstract = {The Indirect Immunofluorescence (IIF) protocol applied on Human Epithelial type 2 (HEp-2) cells is the current gold standard for the Antinuclear Antibody (ANA) test. The formulation of the diagnosis requires the visual analysis of a patient’s specimen under a fluorescence microscope in order to recognize the cells’ staining pattern which could be related to a connective tissue disease. This analysis is time consuming and error prone, thus in the recent past we have witnessed a growing interest in the pattern recognition scientific community directed at the development of methods for supporting this complex task. The main driver of the interest towards this problem is represented by the series of international benchmarking initiatives organized in the last four years that allowed dozens of research groups to propose innovative methodologies for HEp-2 cells’ staining pattern classification. In this paper we update the state of the art on HEp-2 cells and specimens classification, by analyzing the performance achieved by the methods participating the contest on Performance Evaluation of IIF Image Analysis Systems, hosted by the 22nd edition of the International Conference on Pattern Recognition ICPR 2014, and to the Executable Thematic Special Issue of Pattern Recognition Letters on Pattern Recognition Techniques for IIF Images Analysis, and by highlighting the trends in the design of the best performing methods.},
note = {Pattern recognition Techniques for Indirect Immunofluorescence Images Analysis},
keywords = {Computer Aided Diagnoses},
pubstate = {published},
tppubtype = {article}
}
The Indirect Immunofluorescence (IIF) protocol applied on Human Epithelial type 2 (HEp-2) cells is the current gold standard for the Antinuclear Antibody (ANA) test. The formulation of the diagnosis requires the visual analysis of a patient’s specimen under a fluorescence microscope in order to recognize the cells’ staining pattern which could be related to a connective tissue disease. This analysis is time consuming and error prone, thus in the recent past we have witnessed a growing interest in the pattern recognition scientific community directed at the development of methods for supporting this complex task. The main driver of the interest towards this problem is represented by the series of international benchmarking initiatives organized in the last four years that allowed dozens of research groups to propose innovative methodologies for HEp-2 cells’ staining pattern classification. In this paper we update the state of the art on HEp-2 cells and specimens classification, by analyzing the performance achieved by the methods participating the contest on Performance Evaluation of IIF Image Analysis Systems, hosted by the 22nd edition of the International Conference on Pattern Recognition ICPR 2014, and to the Executable Thematic Special Issue of Pattern Recognition Letters on Pattern Recognition Techniques for IIF Images Analysis, and by highlighting the trends in the design of the best performing methods. |
Luc Brun; Gennaro Percannella; Alessia Saggese; Mario Vento Action recognition by using kernels on aclets sequences Journal Article Computer Vision and Image Understanding, 144 , pp. 3 - 13, 2016, ISSN: 1077-3142, (Individual and Group Activities in Video Event Analysis). Abstract | BibTeX | Tag: Soft assignment | Links: @article{Brun20163,
title = {Action recognition by using kernels on aclets sequences},
author = {Luc Brun and Gennaro Percannella and Alessia Saggese and Mario Vento},
url = {http://www.sciencedirect.com/science/article/pii/S1077314215001988},
doi = {http://dx.doi.org/10.1016/j.cviu.2015.09.003},
issn = {1077-3142},
year = {2016},
date = {2016-01-01},
journal = {Computer Vision and Image Understanding},
volume = {144},
pages = {3 - 13},
abstract = {In this paper we propose a method for human action recognition based on a string kernel framework. An action is represented as a string, where each symbol composing it is associated to an aclet, that is an atomic unit of the action encoding a feature vector extracted from raw data. In this way, measuring similarities between actions leads to design a similarity measure between strings. We propose to define this string’s similarity using the global alignment kernel framework. In this context, the similarity between two aclets is computed by a novel soft evaluation method based on an enhanced gaussian kernel. The main advantage of the proposed approach lies in its ability to effectively deal with actions of different lengths or different temporal scales as well as with noise introduced during the features extraction step. The proposed method has been tested over three publicly available datasets, namely the MIVIA, the CAD and the MHAD, and the obtained results, compared with several state of the art approaches, confirm the effectiveness and the applicability of our system in real environments, where unexperienced operators can easily configure it.},
note = {Individual and Group Activities in Video Event Analysis},
keywords = {Soft assignment},
pubstate = {published},
tppubtype = {article}
}
In this paper we propose a method for human action recognition based on a string kernel framework. An action is represented as a string, where each symbol composing it is associated to an aclet, that is an atomic unit of the action encoding a feature vector extracted from raw data. In this way, measuring similarities between actions leads to design a similarity measure between strings. We propose to define this string’s similarity using the global alignment kernel framework. In this context, the similarity between two aclets is computed by a novel soft evaluation method based on an enhanced gaussian kernel. The main advantage of the proposed approach lies in its ability to effectively deal with actions of different lengths or different temporal scales as well as with noise introduced during the features extraction step. The proposed method has been tested over three publicly available datasets, namely the MIVIA, the CAD and the MHAD, and the obtained results, compared with several state of the art approaches, confirm the effectiveness and the applicability of our system in real environments, where unexperienced operators can easily configure it. |
2015
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Pasquale Foggia; Nicolai Petkov; Alessia Saggese; Nicola Strisciuglio; Mario Vento Audio surveillance of roads: a system for detecting anomalous sounds Journal Article IEEE Transactions on Intelligent Transportation Systems, 17 , 2015. BibTeX | Tag: Audio analysis and interpretation | Links: @article{FoggiaRoads2015,
title = {Audio surveillance of roads: a system for detecting anomalous sounds},
author = {Pasquale Foggia and Nicolai Petkov and Alessia Saggese and Nicola Strisciuglio and Mario Vento},
doi = {10.1109/TITS.2015.2470216},
year = {2015},
date = {2015-11-03},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {17},
keywords = {Audio analysis and interpretation},
pubstate = {published},
tppubtype = {article}
}
|
Luc Brun; Gennaro Percannella; Alessia Saggese; Mario Vento Action recognition by using kernels on aclets sequences Journal Article Computer Vision and Image Understanding, 2015. BibTeX | Tag: Video analysis and interpretation | Links: @article{BrunAction2015,
title = {Action recognition by using kernels on aclets sequences},
author = {Luc Brun and Gennaro Percannella and Alessia Saggese and Mario Vento},
doi = {10.1016/j.cviu.2015.09.003},
year = {2015},
date = {2015-09-24},
journal = {Computer Vision and Image Understanding},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {article}
}
|
Benoit Gaüzère; Pierluigi Ritrovato; Alessia Saggese; Mario Vento Human tracking using a top-down and knowledge based approach Conference International Conference on Image Analysis and Processing, 2015, 2015. BibTeX | Tag: Video analysis and interpretation @conference{Gauzere15,
title = {Human tracking using a top-down and knowledge based approach},
author = {Benoit Gaüzère and Pierluigi Ritrovato and Alessia Saggese and Mario Vento},
year = {2015},
date = {2015-09-07},
booktitle = {International Conference on Image Analysis and Processing, 2015},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {conference}
}
|
Duber Martinez; Alessia Saggese; Mario Vento; Humberto Loaiza; Eduardo Caicedo Locally adapted gain control for reliable foreground detection Conference Proceedings of the International Conference on Computer Analysis of Images and Patterns, 2015, 2015. BibTeX | Tag: Image analysis and recognition @conference{Martinez15,
title = {Locally adapted gain control for reliable foreground detection},
author = {Duber Martinez and Alessia Saggese and Mario Vento and Humberto Loaiza and Eduardo Caicedo},
year = {2015},
date = {2015-09-03},
booktitle = {Proceedings of the International Conference on Computer Analysis of Images and Patterns, 2015},
keywords = {Image analysis and recognition},
pubstate = {published},
tppubtype = {conference}
}
|
Pasquale Foggia; Benoit Gaüzère; Alessia Saggese; Mario Vento Human action recognition using an improved string edit distance Conference 12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015, 2015. BibTeX | Tag: Video analysis and interpretation @conference{FoggiaActionEdit15,
title = {Human action recognition using an improved string edit distance},
author = {Pasquale Foggia and Benoit Gaüzère and Alessia Saggese and Mario Vento},
year = {2015},
date = {2015-08-27},
booktitle = {12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015},
journal = {1},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {conference}
}
|
Pasquale Foggia; Nicolai Petkov; Alessia Saggese; Nicola Strisciuglio; Mario Vento Car crashes detection by audio analysis in crowded roads Conference 2th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015, 2015. BibTeX | Tag: Audio analysis and interpretation @conference{FoggiaAvssAudio15,
title = {Car crashes detection by audio analysis in crowded roads},
author = {Pasquale Foggia and Nicolai Petkov and Alessia Saggese and Nicola Strisciuglio and Mario Vento},
year = {2015},
date = {2015-08-26},
booktitle = {2th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015},
keywords = {Audio analysis and interpretation},
pubstate = {published},
tppubtype = {conference}
}
|
Vincenzo Carletti; Pasquale Foggia; Antonio Greco; Alessia Saggese; Mario Vento Automatic detection of long-term parked cars Conference 12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015, 2015. BibTeX | Tag: Video analysis and interpretation @conference{CarlettiPark15,
title = {Automatic detection of long-term parked cars},
author = {Vincenzo Carletti and Pasquale Foggia and Antonio Greco and Alessia Saggese and Mario Vento},
year = {2015},
date = {2015-08-25},
booktitle = {12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {conference}
}
|
Pasquale Foggia; Nicolai Petkov; Alessia Saggese; Nicola Strisciuglio; Mario Vento Reliable Detection of Audio Events in Highly Noisy Environments Journal Article Pattern Recognition Letters, 2015, ISSN: 0167-8655. BibTeX | Tag: Audio analysis and interpretation, Classification Paradigms | Links: @article{Foggia2015,
title = {Reliable Detection of Audio Events in Highly Noisy Environments},
author = {Pasquale Foggia and Nicolai Petkov and Alessia Saggese and Nicola Strisciuglio and Mario Vento},
url = {http://www.sciencedirect.com/science/article/pii/S0167865515001981},
issn = {0167-8655},
year = {2015},
date = {2015-07-09},
journal = {Pattern Recognition Letters},
keywords = {Audio analysis and interpretation, Classification Paradigms},
pubstate = {published},
tppubtype = {article}
}
|
Vincenzo Carletti; Pasquale Foggia; Alessia Saggese; Mario Vento A fast subgraph isomorphism algorithm for social networks graphs Conference Proceedings of the International Workshop on Social Network Analysis, 2015, 2015. BibTeX | Tag: Graph based classification and learning @conference{CarlettiARS15,
title = {A fast subgraph isomorphism algorithm for social networks graphs},
author = {Vincenzo Carletti and Pasquale Foggia and Alessia Saggese and Mario Vento},
year = {2015},
date = {2015-04-01},
booktitle = {Proceedings of the International Workshop on Social Network Analysis, 2015},
keywords = {Graph based classification and learning},
pubstate = {published},
tppubtype = {conference}
}
|
Giovanni Acampora; Pasquale Foggia; Alessia Saggese; Mario Vento A Hierarchical Neuro-Fuzzy Architecture for Human Behavior Analysis Journal Article Information Sciences, 2015. Abstract | BibTeX | Tag: Video analysis and interpretation | Links: @article{inf_sci15,
title = {A Hierarchical Neuro-Fuzzy Architecture for Human Behavior Analysis},
author = {Giovanni Acampora and Pasquale Foggia and Alessia Saggese and Mario Vento},
editor = {Elsevier},
url = {http://dx.doi.org/10.1016/j.ins.2015.03.021},
year = {2015},
date = {2015-03-20},
journal = {Information Sciences},
abstract = {Analysis and detection of human behaviors from video sequences has became recently a very hot research topic in computer vision and artificial intelligence. Indeed, human behavior understanding plays a fundamental role in several innovative application domains such as smart video surveillance, ambient intelligence and content-based video information retrieval. However, the uncertainty and vagueness that typically characterize human daily activities make frameworks for human behavior analysis (HBA) hard to design and develop. In order to bridge this gap, this paper proposes a hierarchical architecture, based on a tracking algorithm, time-delay neural networks and fuzzy inference systems, aimed at improving the performance of current HBA systems in terms of scalability, robustness and effectiveness in behavior detection. Precisely, the joint use of the aforementioned methodologies enables both a quantitative and qualitative behavioral analysis that efficiently face the intrinsic people/objects tracking imprecision and provide context aware and semantic capabilities for better identifying a given activity. The validity and effectiveness of the proposed framework have been verified by using the well-known CAVIAR dataset and comparing our system’s performance with other similar approaches working on the same dataset.},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {article}
}
Analysis and detection of human behaviors from video sequences has became recently a very hot research topic in computer vision and artificial intelligence. Indeed, human behavior understanding plays a fundamental role in several innovative application domains such as smart video surveillance, ambient intelligence and content-based video information retrieval. However, the uncertainty and vagueness that typically characterize human daily activities make frameworks for human behavior analysis (HBA) hard to design and develop. In order to bridge this gap, this paper proposes a hierarchical architecture, based on a tracking algorithm, time-delay neural networks and fuzzy inference systems, aimed at improving the performance of current HBA systems in terms of scalability, robustness and effectiveness in behavior detection. Precisely, the joint use of the aforementioned methodologies enables both a quantitative and qualitative behavioral analysis that efficiently face the intrinsic people/objects tracking imprecision and provide context aware and semantic capabilities for better identifying a given activity. The validity and effectiveness of the proposed framework have been verified by using the well-known CAVIAR dataset and comparing our system’s performance with other similar approaches working on the same dataset. |
Luc Brun; Pasquale Foggia; Alessia Saggese; Mario Vento Recognition of human actions using edit distance on aclet strings Inproceedings VISAPP 2015, 2015. BibTeX | Tag: Video analysis and interpretation @inproceedings{visapp15_ed,
title = {Recognition of human actions using edit distance on aclet strings},
author = {Luc Brun and Pasquale Foggia and Alessia Saggese and Mario Vento},
year = {2015},
date = {2015-03-13},
booktitle = {VISAPP 2015},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Pasquale Foggia; Antonio Greco; Alessia Saggese; Mario Vento A method for detecting long term left baggage based on heat map Inproceedings VISAPP 2015, 2015. BibTeX | Tag: Video analysis and interpretation @inproceedings{visapp15_lb,
title = {A method for detecting long term left baggage based on heat map},
author = {Pasquale Foggia and Antonio Greco and Alessia Saggese and Mario Vento},
year = {2015},
date = {2015-03-12},
booktitle = {VISAPP 2015},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Antonio d'Acierno; Alessia Saggese; Mario Vento Designing Huge Repositories of Moving Vehicles Trajectories for Efficient Extraction of Semantic Data Journal Article IEEE Transactions on Intelligent Transportation Systems, 2015. BibTeX | Tag: Video analysis and interpretation | Links: @article{its2015,
title = {Designing Huge Repositories of Moving Vehicles Trajectories for Efficient Extraction of Semantic Data},
author = {Antonio d'Acierno and Alessia Saggese and Mario Vento},
editor = {IEEE},
url = {http://dx.doi.org/10.1109/TITS.2015.2390652},
year = {2015},
date = {2015-02-26},
journal = {IEEE Transactions on Intelligent Transportation Systems},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {article}
}
|
Pasquale Foggia; Alessia Saggese; Mario Vento Real-time Fire Detection for Video Surveillance Applications using a Combination of Experts based on Color, Shape and Motion Journal Article IEEE Transactions on Circuits and Systems for Video Technology, 2015. BibTeX | Tag: Video analysis and interpretation | Links: @article{csvt2015_fire,
title = {Real-time Fire Detection for Video Surveillance Applications using a Combination of Experts based on Color, Shape and Motion},
author = {Pasquale Foggia and Alessia Saggese and Mario Vento},
editor = {IEEE},
url = {http://dx.doi.org/10.1109/TCSVT.2015.2392531},
year = {2015},
date = {2015-01-14},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {article}
}
|
2014
|
Rosario Di Lascio; Antonio Greco; Alessia Saggese; Mario Vento Improving fire detection reliability by a combination of videoanalytics Incollection Publishing, Springer International (Ed.): Image Analysis and Recognition, pp. 477-484, 2014, ISBN: 978-3-319-11757-7. BibTeX | Tag: Video analysis and interpretation | Links: @incollection{iciar2014,
title = {Improving fire detection reliability by a combination of videoanalytics},
author = {Rosario Di Lascio and Antonio Greco and Alessia Saggese and Mario Vento},
editor = {Springer International Publishing},
url = {http://dx.doi.org/10.1007/978-3-319-11758-4_52},
isbn = {978-3-319-11757-7},
year = {2014},
date = {2014-10-15},
booktitle = {Image Analysis and Recognition},
pages = {477-484},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {incollection}
}
|
Pasquale Foggia; Alessia Saggese; Nicola Strisciuglio; Mario Vento Cascade Classifiers Trained on Gammatonegrams for Reliably Detecting Audio Events Inproceedings IEEE, (Ed.): IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2014), 2014, ISBN: 978-1-4799-4871-0/14. BibTeX | Tag: Audio analysis and interpretation @inproceedings{avss14_audio,
title = {Cascade Classifiers Trained on Gammatonegrams for Reliably Detecting Audio Events},
author = {Pasquale Foggia and Alessia Saggese and Nicola Strisciuglio and Mario Vento},
editor = {IEEE},
isbn = {978-1-4799-4871-0/14},
year = {2014},
date = {2014-08-29},
booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2014)},
keywords = {Audio analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Pasquale Foggia; Alessia Saggese; Nicola Strisciuglio; Mario Vento Exploiting the Deep Learning Paradigm for Recognizing Human Actions Inproceedings IEEE, (Ed.): IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2014), 2014, ISBN: 978-1-4799-4871-0/14. BibTeX | Tag: Video analysis and interpretation @inproceedings{avss14_deep,
title = {Exploiting the Deep Learning Paradigm for Recognizing Human Actions},
author = {Pasquale Foggia and Alessia Saggese and Nicola Strisciuglio and Mario Vento},
editor = {IEEE},
isbn = {978-1-4799-4871-0/14},
year = {2014},
date = {2014-08-29},
booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2014)},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Luc Brun; Gennaro Percannella; Alessia Saggese; Mario Vento HacK: A System for the Recognition of Human Actions by Kernels of Visual Strings Inproceedings IEEE, (Ed.): IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2014), 2014, ISBN: 978-1-4799-4871-0/14. BibTeX | Tag: Video analysis and interpretation @inproceedings{avss14_string,
title = {HacK: A System for the Recognition of Human Actions by Kernels of Visual Strings},
author = {Luc Brun and Gennaro Percannella and Alessia Saggese and Mario Vento},
editor = {IEEE},
isbn = {978-1-4799-4871-0/14},
year = {2014},
date = {2014-08-29},
booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2014)},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Luc Brun; Benito Cappellania; Alessia Saggese; Mario Vento Detection of Anomalous Driving Behaviors by Unsupervised Learning of Graphs Inproceedings IEEE, (Ed.): IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2014), 2014, ISBN: 978-1-4799-4871-0/14. BibTeX | Tag: Video analysis and interpretation @inproceedings{avss14_vrs1,
title = {Detection of Anomalous Driving Behaviors by Unsupervised Learning of Graphs},
author = {Luc Brun and Benito Cappellania and Alessia Saggese and Mario Vento},
editor = {IEEE},
isbn = {978-1-4799-4871-0/14},
year = {2014},
date = {2014-08-29},
booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2014)},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Luc Brun; Alessia Saggese; Mario Vento A Reliable String Kernel based Approach for Solving Queries by Sketch Inproceedings IEEE, (Ed.): IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2014), 2014, ISBN: 978-1-4799-4871-0/14. BibTeX | Tag: Video analysis and interpretation @inproceedings{avss14_vrs2,
title = {A Reliable String Kernel based Approach for Solving Queries by Sketch},
author = {Luc Brun and Alessia Saggese and Mario Vento},
editor = {IEEE},
isbn = {978-1-4799-4871-0/14},
year = {2014},
date = {2014-08-29},
booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2014)},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Alessia Saggese; Luc Brun; Mario Vento Detecting and indexing moving objects for Behavior Analysis by Video and Audio Interpretation Journal Article Electronic Letters on Computer Vision and Image Analysis, 13 (2), 2014, ISSN: 1577-5097. BibTeX | Tag: Audio analysis and interpretation, Video analysis and interpretation | Links: @article{elcvia_14,
title = {Detecting and indexing moving objects for Behavior Analysis by Video and Audio Interpretation},
author = {Alessia Saggese and Luc Brun and Mario Vento},
url = {http://elcvia.cvc.uab.es/article/view/603},
issn = {1577-5097},
year = {2014},
date = {2014-06-07},
journal = {Electronic Letters on Computer Vision and Image Analysis},
volume = {13},
number = {2},
keywords = {Audio analysis and interpretation, Video analysis and interpretation},
pubstate = {published},
tppubtype = {article}
}
|
Alessia Saggese Behavior Analysis Book LAP LAMBERT Academic Publishing, 2014, ISBN: 978-3659529634. BibTeX | Tag: Audio analysis and interpretation, Image analysis and recognition, Video analysis and interpretation | Links: @book{saggese2014,
title = {Behavior Analysis},
author = {Alessia Saggese},
editor = {LAP LAMBERT Academic Publishing},
url = {http://www.amazon.it/Behavior-Analysis-Detecting-indexing-Interpretation/dp/365952963X/ref=sr_1_1?ie=UTF8&qid=1398521162&sr=8-1&keywords=saggese+alessia},
isbn = {978-3659529634},
year = {2014},
date = {2014-04-10},
publisher = {LAP LAMBERT Academic Publishing},
keywords = {Audio analysis and interpretation, Image analysis and recognition, Video analysis and interpretation},
pubstate = {published},
tppubtype = {book}
}
|
Pasquale Foggia; Gennaro Percannella; Alessia Saggese; Mario Vento Pattern recognition in stained HEp-2 cells: Where are we now? Journal Article Pattern Recognition, 2014, ISSN: 0031-3203. BibTeX | Tag: Image analysis and recognition | Links: @article{hep2_2014_pr,
title = {Pattern recognition in stained HEp-2 cells: Where are we now?},
author = {Pasquale Foggia and Gennaro Percannella and Alessia Saggese and Mario Vento},
url = {http://www.sciencedirect.com/science/article/pii/S0031320314000284},
issn = {0031-3203},
year = {2014},
date = {2014-02-12},
journal = {Pattern Recognition},
keywords = {Image analysis and recognition},
pubstate = {published},
tppubtype = {article}
}
|
Luc Brun; Alessia Saggese; Mario Vento Dynamic Scene Understanding for behavior analysis based on string kernels Journal Article Circuits and Systems for Video Technology, IEEE Transactions on, 24 (10), pp. 1669 - 1681, 2014, ISSN: 1051-8215. Abstract | BibTeX | Tag: Video analysis and interpretation | Links: @article{6727519,
title = {Dynamic Scene Understanding for behavior analysis based on string kernels},
author = {Luc Brun and Alessia Saggese and Mario Vento},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6727519&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6913590%29},
issn = {1051-8215},
year = {2014},
date = {2014-01-01},
journal = {Circuits and Systems for Video Technology, IEEE Transactions on},
volume = {24},
number = {10},
pages = {1669 - 1681},
abstract = {This paper aims at dynamically understanding the properties of a scene from the analysis of moving object trajectories. Two different applications are proposed: the former is devoted to identify abnormal behaviors, while the latter allows to extract the (k) , most of the similar trajectories to the one hand-drawn by an human operator. A set of normal trajectories’ models is extracted using a novel unsupervised learning technique: the scene is adaptively partitioned into zones using the distribution of the training set and each trajectory is represented as a sequence of symbols by considering positional information (the zones crossed in the scene), speed, and shape. The main novelty is the use of a kernel-based approach for evaluating the similarity between the trajectories. Furthermore, we define a novel and efficient kernel-based clustering algorithm, aimed at obtaining groups of normal trajectories. Experimentations, conducted over three standard data sets, confirm the effectiveness of the proposed approach.},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {article}
}
This paper aims at dynamically understanding the properties of a scene from the analysis of moving object trajectories. Two different applications are proposed: the former is devoted to identify abnormal behaviors, while the latter allows to extract the (k) , most of the similar trajectories to the one hand-drawn by an human operator. A set of normal trajectories’ models is extracted using a novel unsupervised learning technique: the scene is adaptively partitioned into zones using the distribution of the training set and each trajectory is represented as a sequence of symbols by considering positional information (the zones crossed in the scene), speed, and shape. The main novelty is the use of a kernel-based approach for evaluating the similarity between the trajectories. Furthermore, we define a novel and efficient kernel-based clustering algorithm, aimed at obtaining groups of normal trajectories. Experimentations, conducted over three standard data sets, confirm the effectiveness of the proposed approach. |
2013
|
Pasquale Foggia; Gennaro Percannella; Alessia Saggese; Mario Vento Recognizing Human Actions by a bag of visual words Inproceedings IEEE International Conference on Systems, Man and Cybernetics, IEEE SMC 2013, 2013. BibTeX | Tag: Video analysis and interpretation @inproceedings{smc-13,
title = {Recognizing Human Actions by a bag of visual words},
author = {Pasquale Foggia and Gennaro Percannella and Alessia Saggese and Mario Vento},
year = {2013},
date = {2013-10-15},
booktitle = {IEEE International Conference on Systems, Man and Cybernetics, IEEE SMC 2013},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Vincenzo Carletti; Pasquale Foggia; Gennaro Percannella; Alessia Saggese; Mario Vento Recognition of human actions from RGB-D videos using a reject option Inproceedings International Workshop on Social Behaviour Analysis, SBA, pp. 436-445, Springer Berlin Heidelberg, 2013, ISBN: 978-3-642-41189-2. BibTeX | Tag: Video analysis and interpretation @inproceedings{sba-13,
title = {Recognition of human actions from RGB-D videos using a reject option},
author = {Vincenzo Carletti and Pasquale Foggia and Gennaro Percannella and Alessia Saggese and Mario Vento},
isbn = {978-3-642-41189-2},
year = {2013},
date = {2013-09-10},
booktitle = {International Workshop on Social Behaviour Analysis, SBA},
volume = {8158},
pages = {436-445},
publisher = {Springer Berlin Heidelberg},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Vincenzo Carletti; Pasquale Foggia; Gennaro Percannella; Alessia Saggese; Nicola Strisciuglio; Mario Vento Audio Surveillance Using a Bag of Aural Words Classifier Inproceedings IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2013), pp. 81-86, 2013, ISBN: 10.1109/AVSS.2013.6636620. BibTeX | Tag: Audio analysis and interpretation @inproceedings{avss13,
title = {Audio Surveillance Using a Bag of Aural Words Classifier},
author = {Vincenzo Carletti and Pasquale Foggia and Gennaro Percannella and Alessia Saggese and Nicola Strisciuglio and Mario Vento},
isbn = {10.1109/AVSS.2013.6636620},
year = {2013},
date = {2013-08-28},
booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2013)},
pages = {81-86},
keywords = {Audio analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Rosario Di Lascio; Pasquale Foggia; Gennaro Percannella; Alessia Saggese; Mario Vento A real time algorithm for people tracking using contextual reasoning Journal Article Computer Vision and Image Understanding, 117 (8), pp. 892 - 908, 2013, ISBN: 1077-3142. Abstract | BibTeX | Tag: Video analysis and interpretation | Links: @article{cviu2013,
title = {A real time algorithm for people tracking using contextual reasoning},
author = {Rosario Di Lascio and Pasquale Foggia and Gennaro Percannella and Alessia Saggese and Mario Vento},
url = {http://www.sciencedirect.com/science/article/pii/S1077314213000908},
isbn = {1077-3142},
year = {2013},
date = {2013-04-30},
journal = {Computer Vision and Image Understanding},
volume = {117},
number = {8},
pages = {892 - 908},
abstract = {In this paper we present a real-time tracking algorithm that is able to deal with complex occlusions involving a plurality of moving objects simultaneously. The rationale is grounded on a suitable representation and exploitation of the recent history of each single moving object being tracked. The object history is encoded using a state, and the transitions among the states are described through a Finite State Automata (FSA). In presence of complex situations the tracking is properly solved by making the FSA’s of the involved objects interact with each other. This is the way for basing the tracking decisions not only on the information present in the current frame, but also on conditions that have been observed more stably over a longer time span. The object history can be used to reliably discern the occurrence of the most common problems affecting object detection, making this method particularly robust in complex scenarios. An experimental evaluation of the proposed approach has been made on two publicly available datasets, the ISSIA Soccer Dataset and the PETS 2010 database.},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {article}
}
In this paper we present a real-time tracking algorithm that is able to deal with complex occlusions involving a plurality of moving objects simultaneously. The rationale is grounded on a suitable representation and exploitation of the recent history of each single moving object being tracked. The object history is encoded using a state, and the transitions among the states are described through a Finite State Automata (FSA). In presence of complex situations the tracking is properly solved by making the FSA’s of the involved objects interact with each other. This is the way for basing the tracking decisions not only on the information present in the current frame, but also on conditions that have been observed more stably over a longer time span. The object history can be used to reliably discern the occurrence of the most common problems affecting object detection, making this method particularly robust in complex scenarios. An experimental evaluation of the proposed approach has been made on two publicly available datasets, the ISSIA Soccer Dataset and the PETS 2010 database. |
Pasquale Foggia; Gennaro Percannella; Alessia Saggese; Mario Vento Real-Time Tracking of Single People and Groups Simultaneously by Contextual Graph-Based Reasoning Dealing Complex Occlusions Inproceedings Proceedings of the IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), pp. 29 - 36, IEEE, 2013, ISSN: 2157-491X. BibTeX | Tag: Video analysis and interpretation @inproceedings{pets13,
title = {Real-Time Tracking of Single People and Groups Simultaneously by Contextual Graph-Based Reasoning Dealing Complex Occlusions},
author = {Pasquale Foggia and Gennaro Percannella and Alessia Saggese and Mario Vento},
issn = {2157-491X},
year = {2013},
date = {2013-01-17},
booktitle = {Proceedings of the IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS)},
pages = {29 - 36},
publisher = {IEEE},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Rosario Di Lascio; Pasquale Foggia; Alessia Saggese; Mario Vento A robust people tracking algorithm using contextual reasoning for recovering detection errors Book Chapter Heidelberg, Springer-Verlag Berlin (Ed.): CCIS 359 , pp. 225-241, Springer Berlin Heidelberg, 2013, ISBN: 978-3-642-38240-6. BibTeX | Tag: Video analysis and interpretation @inbook{visigrapp-13,
title = {A robust people tracking algorithm using contextual reasoning for recovering detection errors},
author = {Rosario Di Lascio and Pasquale Foggia and Alessia Saggese and Mario Vento},
editor = {Springer-Verlag Berlin Heidelberg},
isbn = {978-3-642-38240-6},
year = {2013},
date = {2013-01-01},
volume = {CCIS 359},
pages = {225-241},
publisher = {Springer Berlin Heidelberg},
series = {Communications in Computer and Information VISIGRAPP 2012},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inbook}
}
|
Luc Brun; Alessia Saggese; Mario Vento Learning and classification of car trajectories in road video by string kernels Inproceedings Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), pp. 709-714, 2013. BibTeX | Tag: Video analysis and interpretation @inproceedings{pets13b,
title = {Learning and classification of car trajectories in road video by string kernels},
author = {Luc Brun and Alessia Saggese and Mario Vento},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP)},
pages = {709-714},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2012
|
Antonio d'Acierno; Marco Leone; Alessia Saggese; Mario Vento An Efficient Bi-Dimensional Indexing Scheme for Three-Dimensional Trajectories Journal Article International Journal On Advances in Intelligent Systems, 5 (3 and 4), pp. 220-233, 2012, ISSN: 1942-2679. BibTeX | Tag: Video analysis and interpretation @article{ijais-12,
title = {An Efficient Bi-Dimensional Indexing Scheme for Three-Dimensional Trajectories},
author = {Antonio d'Acierno and Marco Leone and Alessia Saggese and Mario Vento},
issn = {1942-2679},
year = {2012},
date = {2012-12-20},
journal = {International Journal On Advances in Intelligent Systems},
volume = {5},
number = {3 and 4},
pages = {220-233},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {article}
}
|
Antonio d'Acierno; Marco Leone; Alessia Saggese; Mario Vento An efficient strategy for spatio-temporal data indexing and retrieval Inproceedings Proceedings of the ”International Conference on Knowledge Discovery and Information Retrieval (KDIR)”, pp. 227–232, SciTePress, 2012, ISBN: 978-989-8565-29-7. BibTeX | Tag: Video analysis and interpretation @inproceedings{kdir2012,
title = {An efficient strategy for spatio-temporal data indexing and retrieval},
author = {Antonio d'Acierno and Marco Leone and Alessia Saggese and Mario Vento},
isbn = {978-989-8565-29-7},
year = {2012},
date = {2012-12-02},
booktitle = {Proceedings of the ”International Conference on Knowledge Discovery and Information Retrieval (KDIR)”},
pages = {227–232},
publisher = {SciTePress},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Luc Brun; Alessia Saggese; Mario Vento A clustering algorithm of trajectories for behaviour understanding based on string kernels Inproceedings Proceedings of the Conference on Signal Image Technology & Internet Based Systems (SITIS), pp. 267–274, IEEE, 2012. BibTeX | Tag: Video analysis and interpretation @inproceedings{sitis2012,
title = {A clustering algorithm of trajectories for behaviour understanding based on string kernels},
author = {Luc Brun and Alessia Saggese and Mario Vento},
year = {2012},
date = {2012-11-28},
booktitle = {Proceedings of the Conference on Signal Image Technology & Internet Based Systems (SITIS)},
pages = {267--274},
publisher = {IEEE},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Antonio d'Acierno; Marco Leone; Alessia Saggese; Mario Vento Efficient Extraction of Motion Flow Data From a Repository of Three-Dimensional Trajectories Using Bi-Dimensional Indexes Inproceedings International Conference on Advances in Information Mining and Management (IMMM 2012), 2012. BibTeX | Tag: Video analysis and interpretation @inproceedings{immm-12,
title = {Efficient Extraction of Motion Flow Data From a Repository of Three-Dimensional Trajectories Using Bi-Dimensional Indexes},
author = {Antonio d'Acierno and Marco Leone and Alessia Saggese and Mario Vento},
year = {2012},
date = {2012-10-19},
booktitle = {International Conference on Advances in Information Mining and Management (IMMM 2012)},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Giovanni Acampora; Pasquale Foggia; Alessia Saggese; Mario Vento Combining Neural Networks and Fuzzy Systems for Human Behavior Understanding Inproceedings Proceedings of the "IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)", pp. 88-93, IEEE, 2012, ISBN: 978-1-4673-2499-1. BibTeX | Tag: Video analysis and interpretation @inproceedings{Acampora2012,
title = {Combining Neural Networks and Fuzzy Systems for Human Behavior Understanding},
author = {Giovanni Acampora and Pasquale Foggia and Alessia Saggese and Mario Vento},
isbn = {978-1-4673-2499-1},
year = {2012},
date = {2012-09-21},
booktitle = {Proceedings of the "IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)"},
pages = {88-93},
publisher = {IEEE},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Antonio d'Acierno; Marco Leone; Alessia Saggese; Mario Vento A system for storing and retrieving huge amount of trajectory data, allowing spatio-temporal dynamic queries Inproceedings Proceedings of the "IEEE Conference on Intelligent Transportation Systems (ITSC)", pp. 989–994, 2012, ISSN: 2153-0009. BibTeX | Tag: Video analysis and interpretation @inproceedings{d'Acierno2012,
title = {A system for storing and retrieving huge amount of trajectory data, allowing spatio-temporal dynamic queries},
author = {Antonio d'Acierno and Marco Leone and Alessia Saggese and Mario Vento},
issn = {2153-0009},
year = {2012},
date = {2012-09-19},
booktitle = {Proceedings of the "IEEE Conference on Intelligent Transportation Systems (ITSC)"},
pages = {989–994},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Donatello Conte; Pasquale Foggia; Gennaro Percannella; Alessia Saggese; Mario Vento An ensemble of rejecting classifiers for anomaly detection of audio events Inproceedings Proceedings of the "IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)", pp. 76-81, IEEE, 2012. BibTeX | Tag: Others @inproceedings{Conte2012_audio,
title = {An ensemble of rejecting classifiers for anomaly detection of audio events},
author = {Donatello Conte and Pasquale Foggia and Gennaro Percannella and Alessia Saggese and Mario Vento},
year = {2012},
date = {2012-09-12},
booktitle = {Proceedings of the "IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)"},
pages = {76-81},
publisher = {IEEE},
keywords = {Others},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Rosario Di Lascio; Pasquale Foggia; Alessia Saggese; Mario Vento Tracking Interacting Objects in Complex Situations by using Contextual Reasoning. Inproceedings Csurka, Gabriela; Braz, José (Ed.): Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP), pp. 104-113, SciTePress, 2012, ISSN: 978-989-8565-04-4. BibTeX | Tag: Video analysis and interpretation | Links: @inproceedings{Visapp12,
title = {Tracking Interacting Objects in Complex Situations by using Contextual Reasoning.},
author = {Rosario Di Lascio and Pasquale Foggia and Alessia Saggese and Mario Vento},
editor = {Gabriela Csurka and José Braz},
url = {http://dblp.uni-trier.de/db/conf/visapp/visapp2012-2.html#LascioFSV12},
issn = {978-989-8565-04-4},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP)},
pages = {104-113},
publisher = {SciTePress},
crossref = {conf/visapp/2012-2},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2011
|
Antonio d'Acierno; Alessia Saggese; Mario Vento A Redundant Bi-Dimensional Indexing Scheme for Three-Dimensional Trajectories Inproceedings Proceedings of the 1th Conference on Advances in Information Mining and Management, pp. 73-78, 2011, ISSN: 2326-9332, (Best paper award). BibTeX | Tag: Video analysis and interpretation @inproceedings{d'Acierno2011,
title = {A Redundant Bi-Dimensional Indexing Scheme for Three-Dimensional Trajectories},
author = {Antonio d'Acierno and Alessia Saggese and Mario Vento},
issn = {2326-9332},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of the 1th Conference on Advances in Information Mining and Management},
pages = {73-78},
note = {Best paper award},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
