Object tracking method based on improved particle swarm optimization
-
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。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.
-
[1] 王铭明, 陈涛, 王建立, 等. Mean-shift 跟踪算法及其在光电跟踪系统中的应用[J]. 中国光学, 2014, 7(2):332-338. WANG M M, CHEN T, WANG J L, et al. Mean-shift tracking algorithm and its application in optoelectronic tracking system[J]. Chinese Optics, 2014, 7(2):332-338.(in Chinese)
[2] 闫辉, 许廷发, 吴青青, 等. 多特征融合匹配的多目标跟踪[J]. 中国光学, 2013, 6(2):163-170. YAN H, XU T F, WU Q Q, et al. Multi-object tracking based on multi-feature joint matching[J]. Chinese Optics, 2013, 6(2):163-170.(in Chinese)
[3] 薛陈, 朱明, 陈爱华. 鲁棒的基于改进Mean-shift的目标跟踪[J]. 光学 精密工程, 2010, 18(1):234-239. XUE CH, ZHU M, CHEN A H. Robust object tracking based on improved Mean-shift algorithm[J]. Opt. Precision Eng., 2010, 18(1):234-239.(in Chinese)
[4] 龚俊亮, 何昕, 魏仲慧, 等. 采用改进辅助粒子滤波的红外多目标跟踪[J]. 光学 精密工程, 2012, 20(2):413-421. GONG J L, HE X, WEI ZH H, et al. Multiple infrared target tracking using improved auxiliary particle filter[J]. Opt. Precision Eng., 2012, 20(2):413-421.(in Chinese)
[5] 王万国, 王滨海, 王振利, 等. Mean-shift跟踪算法中核函数参数的评估与分析[J]. 光学与光电技术, 2012, 10(2):80-92. WANG W G, WANG B H, WANG ZH L, et al. Evaluation and analysis of the kernel function parameters in Mean-shift tracking algorithm[J]. Optics and Optoelectronic Technology, 2012, 10(2):80-92.(in Chinese)
[6] 尹宏鹏, 刘兆栋, 罗显科, 等. 一种基于粒子群优化的目标跟踪特征选择算法[J]. 计算机工程与应用, 2013, 49(17):164-168. YIN H P, LIU ZH D, LUO X K, et al. Target tracking feature selection algorithm based on particle swarm optimization[J]. Computer Engineering and Applications, 2013, 49(17):164-168.(in Chinese)
[7] KENNEDY J, EBERHART R. Particle swarm optimization[J]. IEEE, 1995, 4:1942-1948.
[8] CHEN J. PSO algorithm with stochastic inertia weight and its application in clustering[J]. IEEE, 2011, 2:59-62.
[9] 邹德旋, 王鑫, 陈传虎, 等. 基于改进粒子群的虹膜定位算法[J]. 光学 精密工程, 2014, 22(4):1056-1063. ZOU D X, WANG X, CHEN CH H, et al. Iris location algorithm based on improved particle swarm optimization[J]. Opt. Precision Eng., 2014, 22(4):1056-1063.(in Chinese)
[10] 许廷发, 赵思宏, 周生兵, 等. DSP并行系统的并行粒子群优化目标跟踪[J]. 光学 精密工程, 2009, 17(9):2236-2240. XU T F, ZHAO S H, ZHOU SH B, et al. Particle swarm optimizer tracking based on DSP parallel system[J]. Opt. Precision Eng., 2009, 17(9):2236-2240.(in Chinese)
[11] 张超, 李擎, 陈鹏, 等. 一种基于粒子群参数优化的改进蚁群算法及其应用[J]. 北京科技大学学报, 2013, 35(7):955-960. ZHANG CH, LI Q, CHEN P, et al. Improved ant colony optimization based on particle swarm optimization and its application[J]. J. University of Science and Technology Beijing, 2013, 35(7):955-960(in Chinese)
[12] SHI Y, EBERHART R. A modified particle swarm optimizer[C]. Proceedings of the IEEE International Congress on Evolutionary Computation, Anchorage, May 4-9, 1998:69-73.
[13] CHATTERJEE A, SIARRY P. Nonlinear inertia weight variation for dynamic adaption in particle swarm optimization[J]. Computer and Operations Research, 2006, 33(3):859-871.
[14] ALFI A. PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems[J]. Acta Automatica Sinica, 2011, 37(5):541-549.
[15] PLUHACEK M, SENKERIK R, DAVENDRA D, et al. On the behavior and performance of chaos driven PSO algorithm with inertia weight[J]. Computers & Mathematics with Applications, 2013, 66(2):122-134.
[16] 秦华, 韩克祯, 类成新. 用粒子群算法校正三片镜系统的像差[J]. 中国光学, 2013, 6(1):64-72. QIN H, HAN K ZH, LEI CH X. Correction of aberration for three-lens system by particle swarm optimization algorithm[J]. Chinese Optics, 2013, 6(1):64-72.(in Chinese)
[17] DOCTOR S, VENAYAGAMOORTHY G K. Improving the performance of particle swarm optimization using adaptive critics designs[C]. IEEE Swarm Intelligence Symposium, Pasadena, CA, USA, June 8-10, 2005:393-396. -
点击查看大图
计量
- 文章访问数: 1247
- HTML全文浏览量: 200
- PDF下载量: 588
- 被引次数: 0
下载:
