The growing presence of cameras worldwide and the complexity of real-world environments demand robust and responsible video analytics solutions that operate effectively “in the wild”. This workshop, MDB2026, aims to bridge the gap between controlled research settings and unconstrained real-world scenarios. We welcome contributions that address the core challenges of video understanding in such environments, including (but not limited to) object detection, multi-target and multi-camera tracking, re-identification, activity recognition, anomaly detection, and environmental monitoring, all under the demanding conditions of operating in real scenarios in the wild.

In addition to technical aspects, MDB2026 encourages discussions on fairness, transparency, privacy preservation, and accountability in video analytics systems. The workshop emphasizes the importance of methods, datasets, and benchmarks that foster reproducibility, scalability and ethical compliance: in fact, MDB2026 aims to advance the state of the art in video analytics through innovative algorithms, comprehensive evaluation protocols, and open resources, also taking into account the computational requirements. Case studies and demonstrations from real-world domains are strongly encouraged.

MDB 2026 invites submissions of full papers, presenting mature and complete research with substantial contributions and validated results.

Submissions must adhere to the IEEE double-column format and are limited to six pages, including figures, tables, and references. All accepted papers presented at MDB 2026 will be included in the conference proceedings and published in the IEEE Xplore digital library.

The Microsoft CMT service is used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support. The paper review process will be double-blind to ensure fairness and impartiality.