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PAKDD
2009
ACM

Detecting Abnormal Events via Hierarchical Dirichlet Processes

9 years 1 months ago
Detecting Abnormal Events via Hierarchical Dirichlet Processes
Abstract. Detecting abnormal event from video sequences is an important problem in computer vision and pattern recognition and a large number of algorithms have been devised to tackle this problem. Previous state-based approaches all suffer from the problem of deciding the appropriate number of states and it is often difficult to do so except using a trial-and-error approach, which may be infeasible in real-world applications. Yet in this paper, we have proposed a more accurate and flexible algorithm for abnormal event detection from video sequences. Our three-phase approach first builds a set of weak classifiers using Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), and then proposes an ensemble learning algorithm to filter out abnormal events. In the final phase, we will derive abnormal activity models from the normal activity model to reduce the FP (False Positive) rate in an unsupervised manner. The main advantage of our algorithm over previous ones is to natural...
Xian-Xing Zhang, Hua Liu, Yang Gao, Derek Hao Hu
Added 26 Jul 2010
Updated 26 Jul 2010
Type Conference
Year 2009
Where PAKDD
Authors Xian-Xing Zhang, Hua Liu, Yang Gao, Derek Hao Hu
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