Hierarchical graphical models for simultaneous tracking and recognition in wide-area scenes.

We present a unified framework to track multiple people, as well localize, and label their activities, in complex long-duration video sequences. To do this, we focus on two aspects: 1) the influence of tracks on the activities performed by the corresponding actors and 2) the structural relationships across activities. We propose a two-level hierarchical graphical model, which learns the relationship between tracks, relationship between tracks, and their corresponding activity segments, as well as the spatiotemporal relationships across activity segments. Such contextual relationships between tracks and activity segments are exploited at both the levels in the hierarchy for increased robustness. An L1-regularized structure learning approach is proposed for this purpose. While it is well known that availability of the labels and locations of activities can help in determining tracks more accurately and vice-versa, most current approaches have dealt with these problems separately. Inspired by research in the area of biological vision, we propose a bidirectional approach that integrates both bottom-up and top-down processing, i.e., bottom-up recognition of activities using computed tracks and top-down computation of tracks using the obtained recognition. We demonstrate our results on the recent and publicly available UCLA and VIRAT data sets consisting of realistic indoor and outdoor surveillance sequences.