Robust Visual Tracking with SIFT Features and Fragments Based on Particle Swarm Optimization

Abstract

We propose a novel approach for visual tracking based on a particle swarm optimization (PSO) framework using SIFT feature points correspondence and multiple fragments in a candidate target region to cope with the problems of partial occlusions, illumination changes, and large motion changes of the tracked target. Firstly, optimal search in the successive frame tracking process is performed by the PSO algorithm, which guides all particles towards the global optima state based on a fitness function. Then, the SIFT feature information is integrated into the iterative results of PSO to acquire a more accurate tracking state. Secondly, we present an effective appearance model updating criterion, which evaluates which fragments in appearance model need updating at each frame. However, the fragments with occluded parts or low quality measure values are not updated. The method for updating appearance model is introduced to improve the tracking performance. Compared with state-of-the-art algorithms, the proposed method can still stably track the target during the course of long-term partial occlusions using superior fragments of tracked target. The experiment results demonstrate the effectiveness of our algorithm in complex environments where the target object undergoes partial occlusions and large changes in pose and illumination.