An effective method for the abnormal monitoring of stage performance based on visual sensor network. (April 2018)

• Record Type: Journal Article
• Authors: Zhang, Fuquan; Ding, Gangyi; Xu, Lin; Chen, Bo; Li, Zuoyong
• Is Part Of: International journal of distributed sensor networks. Volume 14:Number 4(2018)
• Publication Date: 2018-04
• Subjects: Visual -- sensor network -- stage -- performing behavior -- abnormality -- monitoring method; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147718769573
• Languages: English
• ISSNs: 1550-1329

Abstract

Abnormal monitoring of stage performance plays a vital role in the stage performance. For the real-time stage performance, detection efficiency and accuracy are particularly important. As the traditional monitoring method based on sparse description model to realize abnormal behavior of stage performance did not realize the manifold structure during the performance, the behavior characteristics are sparse, and the decomposition has higher volatility, the recognition accuracy of abnormal behavior is low. Therefore, an abnormal monitoring method of stage performance based on visual sensor network is proposed, the overall structure of the abnormal monitoring system of stage performance based on the vision sensor network is analyzed, the hardware structure and software composition of the system are designed, and the method of monitoring the abnormal behavior of the system is analyzed emphatically. Through the background subtraction, the weighted threshold-based segmentation of the target image from the background image, the chaotic search particle swarm optimization algorithm based on image target detection and tracking algorithm for target tracking by mean shift, the abnormal behavior of local linear embedding and detection method based on sparse representation, a comprehensive analysis of the local manifold structure of sample is set. Enhance the stage performance of abnormal behavior detection efficiency and accuracy. The experimental results show that the proposed method has higher detection efficiency and accuracy and has higher robustness.