Fast Moving Object Tracking Algorithm based on Hybrid Quantum PSO

摘要

Standard particle swarm optimization (PSO) has capacity of local search exploitation and global search exploratio. The population diversity gets easily lost during the latter period of evolution, which means most particles are convergenced into near positions which is the local optimia. In this paper, a Euclid distance based hybird quantum particle swarm optimization (HQPSO) is brought up. Based on the calculation of population diversity, when the diversity is less than thereshold, population division is proposed for seperating population into two sub-populations based on Euclid distance. One sub-population near Euclid center is defined as P_N will evolve according to traditional QPSO, while the other sub-population far away from center named P_F will fly to boundery which is far away from center. In this way, population diversity would promined to get particles convergence into global optima. Benchmark functions are adopted to testify the efficiency of HQPSO. And based on HQPSO Mean shift algorithm is designed for fast moving object tracking to improve tracking efficiency and decrease detection time cost, which will overcome the "tracking lost" problem of Mean Shift algorithm.