Swarm intelligence based dynamic object tracking
Yuhua Zheng, Yan Jing Meng · 2008
This paper presents a new object tracking algorithm by using the particle swarm optimization (PSO), which is a bio-inspired population-based searching algorithm. Firstly the potential solutions of the problem are projected into a state space called solution space where every point in the space presents a potential solution. Then a group of particles are initialized and start searching in this solution space. The swarm particles search for the best solution within this solution space using the Particle Swarm Optimization (PSO) algorithm. An accumulative histogram of the object appearance is applied to build up the fitness function for the interested object pattern. Eventually the swarming particles driven by the fitness function converge to the optimal solution. Experimental results demonstrate that the proposed PSO method is efficient and robust in visual object tracking under dynamic environments