A simplified swarm optimization for object tracking

Moving object tracking in video sequences is an important task in the field of computer vision. In this paper, we propose a new population-based algorithm namely simplified swarm optimization (SSO) for tracking arbitrary objects. In SSO, the object model is first projected into a high-dimensional feature space, then the particles will fly over image pixels to find an optimal match of the target. While searching for the optimum, SSO progressively analyzes the occlusion situation. If any occlusion or disappearance of the target object is detected, the movement rules for the searching particles will be adaptively adjusted to recapture the target object. Experimental results showed that the SSO can robustly track an arbitrary target in various challenging conditions. Furthermore, SSO is capable to have 40% faster in speed and 36% higher in accuracy rate than the traditional PSO for varied environment.