An Adaptive Gradient Enhanced Texture Based Tracking Algorithm for Video Monitoring Applications
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Object tracking is an important technology in video surveillance. The main approach is Mean Shift algorithm and its improved version. Studies show that the traditional Mean Shift algorithm adopts a fixed searching window in the tracking process, which cannot adjust the template adaptively. The improved algorithm, CamShift, overcomes this problem with an adaptively changing searching window. However, these algorithms are both based on color tracking, which requires that the colors of the foreground targets are unique. If the color of the target is similar to the color of the background, tracking errors will occur or tracking targets will be lost. In this study, we developed an adaptive gradient enhanced texture based tracking algorithm for traffic monitoring applications. This algorithm combines the characteristics of the color and texture of objects. The algorithm builds a joint histogram template of color and texture for targeting, which solves the problems of tracking targets losing when the color of the object is similar to the color of the background. The experiments show that the algorithm can improve the accuracy and robustness of object tracking.
Keywordsvideo surveillance object tracking Camshift texture feature
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