Abstract
To improve the accuracy and speed of the global abnormal detection, a novel method based on motion coherence model is here proposed. Specifically, motion features of each tracking objects are firstly extracted; then, global abnormal behavior detection models are learned based on the energy model, dispersion model and Lagrange particle dynamics model respectively; finally, global abnormal behavior is detected and labeled based on the learned three models. The proposed method is conducted on public UMN dataset which demonstrates that the proposed method can improve the accuracy and efficiency of abnormal behavior detection.
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