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We present an integrated framework for simultaneous tracking, group detectionand multi-level activity recognition in crowd videos. Instead of solving theseproblems independently and sequentially, we solve them together in a unifiedframework to utilize the strong correlation that exists among individualmotion, groups, and activities. We explore the hierarchical structure hidden inthe video that connects individuals over time to produce tracks, connectsindividuals to form groups and also connects groups together to form a crowd.We show that estimation of this hidden structure corresponds to trackassociation and group detection. We estimate this hidden structure under alinear programming formulation. The obtained graphical representation isfurther explored to recognize the node values that corresponds to multi-levelactivity recognition. This problem is solved under a structured SVM framework.The results on publicly available dataset show very competitive performance atall levels of granularity with the state-of-the-art batch processing methodsdespite the proposed technique being an online (causal) one.

An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition