An Improved Mean-Shift Tracking Algorithm using PSO-Based Adaptive Feature Selection

Traditional mean-shift tracking algorithm use pre-defined tracking feature. Its trends to lead tracking failure in the complex background scenes and fast-changing background scenes. In this paper, an improved mean-shift tracking algorithm using Particle swarm optimization (PSO) based adaptive feature selection is presented to improve the tracking performance. We assume that the features with best discrimination between object and background are also the best for tracking the object. A two-class variance ratio is employed to measure the discrimination. PSO algorithm is used to optimize the different feature combination to adaptively generate the best tracking feature. Experimental results show that the proposed method can improve the performance of mean-shift tracker significantly in the complex and fast-changing background scenes.