A novel real time video tracking framework using adaptive discrete swarm optimization

A new adaptive discrete swarm optimization (ADSO) is proposed for video tracking.ADSO shows high accuracy rate and fast tracking and relocating speed.Error rate is reduced to 70.21% of Particle Swarm Optimization (PSO).Processing time per frame is reduced to 58.6% of PSO. This paper has proposed a new adaptive discrete swarm optimization (ADSO) for the video tracking framework. Each target object is first presented by a search window with four-dimensional features, which include 2D coordinates of the search window, its width and height. The image in the search window of a target object is extracted to calculate the HSV histograms, which are used to establish a feature model for the target object. Then the particles fly in a sub-search-space to find an optimal match of the target. If any occlusion or disappearance of the target object is detected, the particles will adaptively update their searching strategies in order to recapture the target. The experimental results demonstrate that the ADSO can out-perform the traditional PSO algorithm in the aspects of high accuracy rate and fast tracking and relocating speed.