Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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This paper aims to address the problem of clustering activities captured in surveillance videos for the applications of online normal activity recognition and anomaly detection. A novel framework is developed for automatic activity modelling and anomaly detection without any manual labelling 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 activity representation method as a stochastic sequence of spatio-temporal actions, where we analyse the global structural information of activities using their local action statistics. 2 The natural grouping of activities is discovered through a novel clustering algorithm with unsupervised model selection, named latent Dirichlet Markov clustering LDMC. The approach builds on hidden Markov models HMMs and latent Dirichlet allocation LDA, and overcomes their drawbacks on accuracy, robustness and computational efficiency. 3 A runtime accumulative anomaly measure is introduced to detect abnormal activity, whereas normal activities are recognised 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 activity recognition at the shortest possible time. Experimental results demonstrate the effectiveness and robustness of our approach using noisy and sparse data sets collected from real surveillance scenarios.