Anomaly Localization in Topic-Based Analysis of Surveillance Videos

Topic-models for video analysis have been used for unsupervised identification of normal activity in videos, thereby enabling the detection of anomalous actions. However, while intervals containing anomalies are detected, it has not been possible to localize the anomalous activities in such models. This is a challenging problem as the abnormal content is usually a small fraction of the entire video data and hence distinctions in terms of likelihood are unlikely. Here we propose a methodology to extend the topic based analysis with rich local descriptors incorporating quantized spatio-temporal gradient descriptors with image location and size information. The visual clips over this vocabulary are then represented in latent topic space using models like pLSA. Further, we introduce an algorithm to quantify the anomalous content in a video clip by projecting the learned topic space information. Using the algorithm, we detect whether the video clip is abnormal and if positive, localize the anomaly in spatio-temporal domain. We also contribute one real world surveillance video dataset for comprehensive evaluation of the proposed algorithm. Experiments are presented on the proposed and two other standard surveillance datasets.