Study Of Moving Target Video Monitoring Method

Video surveillance system is widely used in daily life. Moving target video monitoringmethod is one of the key techniques in video monitoring system. Target detection in complexbackground and long-term target tracking is a difficult point in video monitoring method.Target detection by model of local features and target tracking algorithm based ontracking-learning-detection framework is one of the important development direction.In this paper, the target detection algorithm based on implicit shape model and the targettracking algorithm based on TLD framework are studied. This paper first introduces themethod to generate the implicit shape model and the target detection algorithm based on theimplicit shape model. We join the angle information in the calculation of coordinate voting, toenhance the ability to adapt the target rotation. Then we introduce the classical algorithm intarget tracking—Lucas-Kanade algorithm, as well as the framework and idea of TLD trackingalgorithm. On this basis, the implicit shape model is introduced into the TLD trackingalgorithm framework. The target tracking algorithm based on implicit shape model consists ofa Tracker, Detector, Learner, Integrator. When the algorithm is initialized, the originalimplicit shape model of tracking object is initialized. Detector based on this model to detectobject in the input video frames. Tracker based on the contents of the previous frame in theobject frame search object in the adjacent next frame. Integrator fusion tracker output anddetector output to get tracking box position. Learner updates the target model according to theresults of Integrator.Programming the algorithm proposed in this paper, and the target detection and trackingalgorithm performance is tested. Target detection experiment results show that the improvedalgorithm has the strong adaptive ability to target angle change. Target tracking experimentresults show that the target tracking algorithm based on implicit shape model has good antiblocking performance. The target detection and tracking algorithm of this paper has a goodprospect in the neighborhood, parking lot, urban traffic video monitoring system.