This study addresses the problem of automaticanomalydetection for surveillance applications. A general framework for anomalous event detection in uncrowded scenes has been developed which consists of the following k...
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This study addresses the problem of automaticanomalydetection for surveillance applications. A general framework for anomalous event detection in uncrowded scenes has been developed which consists of the following key components: (i) an efficient foreground detection model based on a Gaussian mixture model (GMM), which can selectively update pixel information in each image region;(ii) an adaptive foreground object tracker that combines the merits of Kalman, mean-shift and particle filtering;(iii) a feature clustering algorithm, which can automatically choose the optimal number of clusters in the training data for scene pattern modelling;(iv) a statistical scene modeller based on Bayesian theory and GMM, which combines trajectory-based and region-based information for enhanced anomalydetection. The resulting approach achieves fully unsupervised anomalydetection in surveillance video. The experimental results show improved detection performance compared with the state-of-the-art methods.
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