Exploiting Spatio-Temporal Scene Structure for Wide-Area Activity Analysis in Unconstrained Environments
Surveillance videos in unconstrained environments typically consist of long duration sequences of activities which occur at different spatio-temporal locations and can involve multiple people acting simultaneously. Often, the activities have contextual relationships with one another. Although context has been studied in the past for the purpose of activity recognition, the use of context in recognition of activities in such challenging environments is relatively unexplored. In this paper, we propose a novel method for capturing the spatio-temporal context between activities in a Markov random field. The structure of the MRF is improvised upon during test time and not predefined, unlike many approaches that model the contextual relationships between activities. Given a collection of videos and a set of weak classifiers for individual activities, the spatio-temporal relationships between activities are represented as probabilistic edge weights in the MRF. This model provides a generic representation for an activity sequence that can extend to any number of objects and interactions in a video. We show that the recognition of activities in a video can be posed as an inference problem on the graph. We conduct experiments on the publicly available UCLA office dataset and the VIRAT dataset, to demonstrate the improvement in recognition accuracy using our proposed model as opposed to recognition using state-of-the-art features on individual activity regions.