Multi-swarms Dynamic Convergence Optimization for object tracking

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Swarm intelligence has been applied to many research projects in recent years, many scientists are working on developing the full potential of a self-organized and decentralized system to help solving complex problems. In image processing, it also demonstrates fast and accurate in searching solutions for trajectory clustering and precise object tracking. This paper is aim to introduce a novel multiple particle swarms with dynamic convergence approach for object tracking in complicated environment. Our new approach absorbs the advantages of other multi swarm algorithms to optimize the resources and process iteration. So it can provide more accurate and faster tracking result for both linear and non-linear movement pattern when compared to basic PSO and other PSO based algorithms such as inertia weight PSO and constriction factor PSO. In addition, multiple independent populations will not only inherit each of their own attribute's weights through dynamic range convergence, but also influence by each other's solution effects. The experiments have been conducted with different types of testing videos in real environment. The results examined with different types of moving pattern have demonstrated that the new method required less resources and iteration process and could have better tracking performance and scarcely lost target with diverse interferences.