改进的粒子群优化目标跟踪方法

郭巳秋; 许廷发; 王洪庆; 张一舟; 申子宜

doi:10.3788/CO.20140705.0759

改进的粒子群优化目标跟踪方法

doi: 10.3788/CO.20140705.0759

北京理工大学光电学院光电成像技术与系统教育部重点实验室, 北京 100081

基金项目:

国家自然科学基金资助项目（No.61172178，No.61371132）;国家国际科技合作专项资助项目（No.2014DFR1096）;高等学校博士学科点专项科研基金资助项目（No.20121101110022）

详细信息

作者简介:
郭巳秋（1989- ），女，吉林长春人，硕士研究生，2012年于北京理工大学获得学士学位，主要从事目标检测、跟踪算法方面的研究。

通讯作者:
许廷发，E-mail：xutingfa@163.com

中图分类号: TP391.41
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出版历程
- 收稿日期: 2014-06-18
- 修回日期: 2014-08-16
- 刊出日期: 2014-09-25

Object tracking method based on improved particle swarm optimization

Laboratory of Optoelectronics Imaging Technology and System of Ministry of Education School of OptoeLectronics, Beijing Institute of TechnoLogy, Beijing 100081, China

摘要

摘要: 针对粒子群优化算法应用在目标跟踪时，其惯性权重调节机制的局限性，提出了改进的粒子群优化目标跟踪方法。首先，对目标及粒子群算法中相应参数进行初始化；接着，引入粒子进化率的概念，对惯性权重调节机制进行改进，根据每代每个粒子的不同状态及时调整惯性权重；然后，在更新粒子的速度和位置的同时，更新个体最优解和全局最优解，进行下一次迭代；最后，比较粒子的适应度，选择相似性函数值最大的区域为目标。实验结果表明，该方法与使用自适应惯性权重调节机制的粒子群优化目标跟踪方法相比，减少了获取相同适应度所需的迭代次数，运算效率提高了42.9%。实现了目标在相似性函数出现多峰情况下的准确定位，对目标出现部分遮挡的情况具有很好的适应性。
- 目标跟踪 /
- 粒子群优化 /
- 粒子进化率 /
- 惯性权重
Abstract: To overcome the limitations of inertia weight adjustment mechanism when the particle swarm optimization algorithm is applied to object tracking, an improved particle swarm optimization object tracking algorithm is proposed. Firstly, the object and the parameters in particle swarm optimization algorithm are initialized. Secondly, the inertia weight adjustment mechanism is improved by using the evolution rate of particle, and the inertia weight is achieved by taking the conditions of different particles in each generation into consideration. Then the speed, the position, the individual optimum and the global optimum of the particles are updated simultaneously while the next iteration is proceeding. Finally, the area which has the largest similarity function value is defined as the object by comparing the fitness value of each particle with the others. Experimental results indicate that the method reduces the iterations to obtain the same fitness value, and improves the operation efficiency by 42.9% in comparison with the particle swarm optimization object tracking method which uses self-adapted inertia weight adjustment mechanism. The accurate positioning of the object is achieved in the case of the similarity function presenting multimodal, and the method is well adapted to the situation when partial occlusion occurs in object tracking.
- object tracking /
- particle swarm optimization /
- evolution rate of particle /
- inertia weight

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