Fusing multi-feature for object tracking algorithm based on color and space information

Object tracking using single feature often leads to a poor robustness. Aiming at this,an object tracking algorithm using multiple features fusion based on color and space information was presented. In order to enhance the important features, an adaptive method for choosing object color histogram was proposed to get an accurate color model of the object. Meanwhile, spatiograms were used to obtain spatial layout of these colors for the targets. These features were rationally fused in the framework of particle filter. The uncertainty measurement method was then introduced into features fusion to adjust the relative contributions of different features adaptively, and the robustness of the algorithm was significantly enhanced. Simulation experimental results show that the mean minimum location error of the proposed tracking algorithm is only 6.967 pixel,while that of the signal feature tracking algorithm and the traditional algorithm are 192.576 pixel and 199.464 pixel,respectively, which indicates that the proposed algorithm can track objects with better tracking accuracy and robustness.