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Video Behavior Profiling for Anomaly Detection

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2 Author(s)
Tao Xiang ; Univ. of London, London ; Shaogang Gong

This paper aims to address the problem of modeling video behavior captured in surveillance videos for the applications of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior profiling and online anomaly sampling/detection without any manual labeling of the training data set. The framework consists of the following key components: 1) A compact and effective behavior representation method is developed based on discrete-scene event detection. The similarity between behavior patterns are measured based on modeling each pattern using a Dynamic Bayesian Network (DBN). 2) The natural grouping of behavior patterns is discovered through a novel spectral clustering algorithm with unsupervised model selection and feature selection on the eigenvectors of a normalized affinity matrix. 3) A composite generative behavior model is constructed that is capable of generalizing from a small training set to accommodate variations in unseen normal behavior patterns. 4) A runtime accumulative anomaly measure is introduced to detect abnormal behavior, whereas normal behavior patterns are recognized when sufficient visual evidence has become available based on an online Likelihood Ratio Test (LRT) method. This ensures robust and reliable anomaly detection and normal behavior recognition at the shortest possible time. The effectiveness and robustness of our approach is demonstrated through experiments using noisy and sparse data sets collected from both indoor and outdoor surveillance scenarios. In particular, it is shown that a behavior model trained using an unlabeled data set is superior to those trained using the same but labeled data set in detecting anomaly from an unseen video. The experiments also suggest that our online LRT-based behavior recognition approach is advantageous over the commonly used Maximum Likelihood (ML) method in differentiating ambiguities among different behavior classes observed online.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:30 ,  Issue: 5 )