In this paper we present a novel method for anomalous activity detection using systematic trajectory analysis. First, the visual scene is segmented into constituent regions by attaching importances based on motion dynamics of targets in that scene. Further, a structured representation of these segmented regions in the form of a region association graph (RAG) is constructed. Finally, anomalous activity is detected by benchmarking the target's trajectory against the RAG. We have evaluated our proposed algorithm and compared it against competent baselines using videos from publicly available as well as in-house datasets. Our results indicate high accuracy in localizing anomalous segments and demonstrate that the proposed algorithm has several compelling advantages when applied to scene analysis in autonomous visual surveillance.

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