一种基于混合量子粒子群的快速运动目标跟踪算法研究

Fast Moving Object Tracking Algorithm based on Hybrid Quantum PSO

量子粒子群算法在优化过程中需要权衡局部探索性和全局开拓性,进化后期由于全局开拓能力的丧失使得种群多样性减少,设计了一种基于欧式距离的混合量子粒子群算法,通过计算粒子的种群多样性,当种群多样性低于阈值范围时加入基于欧式距离的种群划分策略划分子种群,从而保证获得全局最优解。利用标准测试函数验证提出的混合量子群算法有效性。提出了基于混合量子粒子群的Mean Shift算法(HQPSO Mean Shift)完成目标快速跟踪,克服传统Mean Shift算法的在跟踪快速移动目标时出现"跟丢"的问题。

Standard particle swarm optimization （ PS0 ） has capacity of local search exploitation and global search exploratio. The pop- ulation diversity gets easily lost during the latter period of evolution, where most particles are convergenced into near positions which is the local optimia. In this paper, a Euclid distance based hybird quantum particle swarm optimization （HQPS0） is brought up. Based on the calculation of population diversity, when the diversity is less than thereshold, population division is proposed for seperating popu- lation into two sub - populations based on Euclid distance. In this way, it will promise population diversity to get convergence into glob- al optima. Benchmark functions are adopted to testify the efficiency of HQPSO. And based on HQPS0 Mean shift algorithm will over- come the ＂tracking lost＂ problem of Mean Shift algorithm.