Research Of Compressive Tracking Algorithm

Recently,visual target tracking is an WCTive branch.It combines computer vision, artificial intelligence, pattern recognition and other technical disciplines. It has broad application prospects in video security monitoring, video compression and retrieval, etc. The system inputs image sequence, the output is the target of various attributes, such as size,position, and so on. Its purpose is to determine the location of the object in image sequence. There are several reasons to bad tracking such as changes in illumination, shape deformation, object partial occlusion, moving, and other sports. Tracking algorithm is usually divided into generating algorithm and discriminant algorithm.Compressive Sensing is rising fast as a signal compression theory. It has broken the Nyquist sampling theorem limiting signal sampling rate,creating a new situation due to its own advantages for the signal processing. Since the theory has been put forth, scholars compete on outstanding contributions to its development. With the gradual improvement of Compressive Sensing theory, its application is no longer limited to static signal, and the unique combination of dynamic tracking algorithm effectively reduce the computational complexity, improve tracking speed,create a new compressive track pattern. The real-time target tracking algorithm based on Compressive Sensing is a novel algorithm(CT), can get accurate tracking effect quickly.Real-time compression tracking algorithm(CT) is put forward by professor kai-hua zhang in 2012, extrWCTing target feature in compressed domain. CT is a simple yet effective and efficient tracking algorithm.It received widespread attention, and subsequently there are various kinds of improved algorithm. including FCT、WCT, and so on. FCT searches in the frame,considering space information.WCT selects the most discriminative features to design an effective appearance model.This article will take FCT as the key research object.Analyzing and comparing the advantages and disadvantages of CT、WCT and FCT, this paper puts forward the improvement on the basis of FCT. In FCT, because of the sparsity of the compression measurement matrix, the spatial information of the sample is neglected, so the feature cannot represent the tracking target well,there is no remedy when tracking error. To address this issue, this paper puts forward an improved fast compressive tracking algorithm considering the sample space information and extrWCTing generalized Haar-like features randomly in block.target motion estimation method is also used to correct target location,as the classifier is wrong. Adjusting the sparse degree of vector in compression measurement matrix and threshold of naive Bayes classifier can realize accurate target tracking. The experimental results show that the improved algorithm compared with FCT performs much better in terms of similarity、success rate and subjective visual perception.