Steps toward a cognitive vision system
AI Magazine
Conceptual representations between video signals and natural language descriptions
Image and Vision Computing
Understanding dynamic scenes based on human sequence evaluation
Image and Vision Computing
A large-scale benchmark dataset for event recognition in surveillance video
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Automatic unconstrained online configuration of a master-slave camera system
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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This contribution aims at assisting video surveillance operators with automatic understanding of situations in videos. The situations comprise many different agents interacting in groups. To this end we extended an existing situation recognition framework based on Situation Graph Trees and Fuzzy Metric Temporal Logic. Non-parametric mean-shift clustering is utilized to support the logic-based inference process for such group-based situations, namely to improve efficiency. Additionally, the underlying knowledge base was augmented to also handle multi-agent queries and the situation inference was adapted to also handle inference for group-based situations. For evaluation the publicly available BEHAVE video dataset was used consisting of partially annotated real video data of persons. The results show that the proposed system is capable of correctly and efficiently understanding such group-based situations.