Study on vision object tracking based on adaptive object segmentation

Abstract

In order to solve the object tracking under occlusion, the adaptively tracking algorithm is proposed based on color features. The object is adaptively divided using fuzzy k-means clustering algorithm, and the sub-regions are weighted with monotone decreasing kernel function. The object model is updated through mean value of sub-regions' colors, so the calculation is simple. During the object tracking, the method of integral matching is used; combining with the adaptive Kalman filter, the object tracking under occlusion is resolved effectively. The experiments show that the new algorithm can track the object exactly.

Self-adaptive threshold tracking algorithm of infrared weak-small targets

“…Detecting IWST is difficult, because it is almost no available information such as shape, size and texture except some gray value [1] . To detect IWST, foreign and domestic scholars put forward many meaningful detection methods, such as the detection algorithm based on background prediction, detection algorithm based on neural network, detection algorithm based on dynamic programming and so on [2] [3] . However, all the algorithms mentioned above are too much complex in the calculation to meet real-time and reliability requirements in practical application.…”