Multi-featured integration annealing particle filtering target tracking

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (6): 164-167.

• 图形、图像、模式识别 • Previous Articles Next Articles

Multi-featured integration annealing particle filtering target tracking

CHU Hongxia^1，2，WANG Kejun¹，WANG Xifeng²，GUO Qingchang¹，HAN Jing²

1.College of Automation，Harbin Engineering University，Harbin 150001，China
2.Department of Electronic Engineering，Heilongjiang Institute of Technology，Harbin 150001，China

Received:1900-01-01 Revised:1900-01-01 Online:2011-02-21 Published:2011-02-21

多特征融合的退火粒子滤波目标跟踪

初红霞^1，2，王科俊¹，王希凤²，郭庆昌¹，韩晶²

1.哈尔滨工程大学自动化学院，哈尔滨 150001
2.黑龙江工程学院电子工程系，哈尔滨 150001

Abstract

Abstract: Proposal distribution of traditional particle filtering has the shortcomings which is lacking of utilizing current observational information.In order to improve the performance of particle filter for target tracking，a particle filter tracking method based on multi-featured integration annealing algorithm is proposed to improve the proposal distribution.The proposed solution gives an answer to the large amount of calculation and lacking of particles under high-dimensional conditions.With adoption of the approach，the better proposal distribution can be generated within the scope of Monte Carlo importance sampling range，and the simple priori probability sampling can also be replaced by annealing likelihood sampling.In the likelihood approximation，image feature attribute of colors and edges integration is applied to generate weight function at different annealing layer by weighing.Experiment shows the method is of higher tracking accuracy and stronger stability when tracking moving objects is under complex situation and occlusion circumstance.

Key words: particle filtering, Simulated Annealing, multi-featured integration, proposal distribution

摘要： 针对传统粒子滤波的建议分布没有利用到当前观测信息的缺点，提出了一种基于多特征融合的退火算法来改进建议分布的粒子滤波跟踪方法。该方法解决了高维状态下计算量大和粒子数匮乏问题。采用退火方法在蒙特卡洛重要采样范围内产生更好的建议分布，并用退火似然性抽样来代替简单的先验概率抽样。在似然逼近中，应用颜色和边缘相融合的图像特征属性在不同的退火层加权来产生权重功能函数。用该方法对复杂背景下和存在遮挡情况下的运动目标进行跟踪，结果表明该方法有较高的跟踪精度和较强的稳定性。

关键词: 粒子滤波, 模拟退火, 多特征融合, 建议分布

CHU Hongxia^1，2，WANG Kejun¹，WANG Xifeng²，GUO Qingchang¹，HAN Jing². Multi-featured integration annealing particle filtering target tracking[J]. Computer Engineering and Applications, 2011, 47(6): 164-167.

初红霞^1，2，王科俊¹，王希凤²，郭庆昌¹，韩晶². 多特征融合的退火粒子滤波目标跟踪[J]. 计算机工程与应用, 2011, 47(6): 164-167.

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Multi-featured integration annealing particle filtering target tracking

多特征融合的退火粒子滤波目标跟踪

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