Research Article
Behavior Clustering for Anomaly Detection
@INPROCEEDINGS{10.1007/978-3-642-23602-0_2, author={Xudong Zhu and Hui Li and Zhijing Liu}, title={Behavior Clustering for Anomaly Detection}, proceedings={Forensics in Telecommunications, Information, and Multimedia. Third International ICST Conference, e-Forensics 2010, Shanghai, China, November 11-12, 2010, Revised Selected Papers}, proceedings_a={E-FORENSICS}, year={2012}, month={10}, keywords={Computer Vision Anomaly Detection Hidden Markov Model Latent Dirichlet Allocation}, doi={10.1007/978-3-642-23602-0_2} }- Xudong Zhu
Hui Li
Zhijing Liu
Year: 2012
Behavior Clustering for Anomaly Detection
E-FORENSICS
Springer
DOI: 10.1007/978-3-642-23602-0_2
Abstract
This paper aims to address the problem of clustering behaviors captured in surveillance videos for the applications of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior modeling and anomaly detection without any manual labeling of the training data set. The framework consists of the following key components: 1) Drawing from natural language processing, we introduce a compact and effective behavior representation method as a stochastic sequence of spatiotemporal events, where we analyze the global structural information of behaviors using their local action statistics. 2) The natural grouping of behaviors is discovered through a novel clustering algorithm with unsupervised model selection. 3) A run-time accumulative anomaly measure is introduced to detect abnormal behaviors, whereas normal behaviors 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. Experimental results demonstrate the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.