Mean Shift Object Tracking Based on Adaptive Multi-Features Fusion

Traditional mean shift tracking algorithm has achieved considerable success in object tracking due to its simplicity and robustness. But, it models the objects to be tracked with single color feature, which leads to that it is more prone to ambiguity, especially if the scene contains other objects characterized by a color distribution similar to that of the object of interest. To address this problem, a formula for target localization with mean shift based on multiple features is deduced. In addition, a method that evaluates the discriminability of each features with respect to foreground to background separability and adaptively calculates the features fusion weight by probability separability criterion is proposed. With the above deduced formula and proposed method, a novel mean shift target tracking algorithm based on adaptive multiple features fusion is presented. The proposed algorithm is run for each feature independently and the output of the mean shift algorithm for each feature is weighted based on the fusion weight. The states of the target in the current frame are computed through the integration of the outputs of mean shift. Experiments are conducted with color sequences and gray sequences, and its results show that the proposed algorithm has better performance than the classical mean shift tracking algorithm.