Video-based activity analysis using the L1 tracker on VIRAT data

Developments in video tracking have addressed various aspects such as target detection, tracking accuracy, algorithm comparison, and implementation methods which are briefly reviewed. However, there are other attributes of full motion video (FMV) tracking that require further investigation for situation awareness of event and activity analysis. Key aspects of activity and behavior analysis include interaction between individuals, groups, and crowds as well as with objects in the environment like vehicles and buildings over a specified time duration as it is typically assumed that the activities of interest include people. In this paper, we explore activity analysis using the L1 tracker over various scenarios in the VIRAT data. Activity analysis extends event detection from tracking accuracy to characterizing number, types, and relationships between actors in analyzing human activities of interest. Relationships include correlation in space and time of actors with other people, objects, vehicles, and facilities (POVF). Event detection is more mature (e.g., based on image exploitation and tracking techniques), while activity analysis (as a higher level fusion function) requires innovative techniques for relationship understanding.