Abstract: Focusing on the issue that the tracking algorithm based on template matching has poor performance in running speed and success rate, a template matching tracking algorithm based on Particle Swarm Optimization (PSO) was proposed. The algorithm took the PSO algorithm as the search strategy of the candidate templates in template matching algorithm, and the target template was updated self-adaptively. Firstly, 30 candidate templates were selected in a search scope and then the individual and global optimal candidate template were selected; secondly, the best candidate template was worked out through the particle swarm optimization and the target is the best one; finally, the target template was updated self-adaptively based on the matching rate of the best candidate template. The theoretical analysis and simulation experiments show that, compared with the tracking algorithm based on template matching and the template matching tracking algorithm based on the rough search and refined by search, the computation of the template matching tracking algorithm based on particle swarm optimization is greatly reduced about 91.1% and 69.8%, and the success rate is 2.02 times and 1.94 times of the primary algorithm. The experiment show that the new algorithm can achieve well real-time tracking and the robustness and accuracy of tracking is greatly improved.

Key words: machine vision, target tracking, template matching, Particle Swarm Optimization (PSO), template update

摘要： 针对基于模板匹配的跟踪算法运行速度较慢、成功率较低的问题,提出了一种基于粒子群优化(PSO)的模板匹配跟踪算法。该算法采用粒子群优化算法作为模板匹配算法候选模板的搜索策略,并采用自适应的更新目标模板。首先,在设定的搜索区域内随机采集30个候选模板,计算出个体最优候选模板和全局最优候选模板;其次,根据粒子群优化算法进行迭代求出匹配值最佳的候选模板即为目标;最后,根据最佳候选模板的匹配值大小来自适应更新目标模板。理论分析和实验仿真表明,与基于模板匹配的跟踪算法和基于粗精搜索的模板匹配跟踪算法相比,基于粒子群优化的模板匹配跟踪算法的计算量平均要少91.1%和69.8%,且成功率为原算法的2.02倍和1.94倍。实验结果表明,基于粒子群优化的模板匹配跟踪算法能实现很好的实时跟踪,并且提高了跟踪的鲁棒性。

关键词: 机器视觉, 目标跟踪, 模板匹配, 粒子群优化, 模板更新