A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
Beam sampling for the infinite hidden Markov model
Proceedings of the 25th international conference on Machine learning
Automated detection of unusual events on stairs
Image and Vision Computing
A dynamic hierarchical clustering method for trajectory-based unusual video event detection
IEEE Transactions on Image Processing
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Infinite Hidden Markov Models for Unusual-Event Detection in Video
IEEE Transactions on Image Processing
Weakly Supervised Learning of a Classifier for Unusual Event Detection
IEEE Transactions on Image Processing
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The multi-rate phenomenon of video unusual event is one of the factors to reduce the detection accuracy of video unusual event. Based on the infinite state Hidden Markov Model (iHMM), a multi-granularity detection algorithm for video unusual event is proposed. This algorithm first effectively extracts the feature sequence from the original data through subspace projection technique. Then the feature sequence is sampling at different time intervals to obtain the multi-rate feature sequences. And these multi-rate feature sequences can be used to construct the different time granularities model in the model training stage, and to find the video unusual event at different time granularities in the detection stage. In parameter learning of iHMM, the Beam sampling and EM is combined to improve the efficiency of the iteratively estimation. The experimental results using the surveillance data of vehicles forbidding section, show that the proposed method can be effectively detect unusual events in a complex outdoor scene.