Cooperative Object Tracking Algorithm Based on Epipolar Geometry Constrained Particle Filter

CHEN Bin; ZHAO Jian-jun; YAO Gang; YANG Li-bin

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Cooperative Object Tracking Algorithm Based on Epipolar Geometry Constrained Particle Filter

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- Cooperative Object Tracking Algorithm Based on Epipolar Geometry Constrained Particle Filter
- Vol. 34, Issue 11, Pages: 53-57(2017)
- 作者机构：
  
  海军航空工程学院兵器科学与技术系,山东,烟台,264001
- 作者简介：
- 基金信息：
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- Published：2017
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陈滨, 赵建军, 姚刚, 杨利斌. 对极几何约束下的粒子滤波目标协同跟踪算法[J]. 微电子学与计算机, 2017, 34(11): 53-57, 62.

CHEN Bin, ZHAO Jian-jun, YAO Gang, et al. Cooperative Object Tracking Algorithm Based on Epipolar Geometry Constrained Particle Filter[J]. 2017, 34(11): 53-57.
陈滨, 赵建军, 姚刚, 杨利斌. 对极几何约束下的粒子滤波目标协同跟踪算法[J]. 微电子学与计算机, 2017, 34(11): 53-57, 62. DOI：

CHEN Bin, ZHAO Jian-jun, YAO Gang, et al. Cooperative Object Tracking Algorithm Based on Epipolar Geometry Constrained Particle Filter[J]. 2017, 34(11): 53-57. DOI：

摘要

为了提高双视角条件下的粒子滤波目标跟踪算法的性能

提出了对极约束下的粒子滤波目标协同跟踪算法.利用对极几何约束

一方面对协同跟踪过程中的目标检测区域进行限制

减少检索空间; 另一方面对粒子滤波目标跟踪算法的状态转移模型进行改进

减少目标在监控重叠区域时

每个摄像头跟踪所需的粒子总数.最后

从帧率、中心误差及覆盖率上

对算法的性能进行分析比较.实验结果表明

算法在时间性能上能满足实时性要求

准确度上较之经典粒子滤波目标跟踪算法

有了明显的提高.

Abstract

In order to improve the performance of particle filter object tracking algorithm based on two-view geometry

cooperative object tracking algorithm based on epipolar geometry constrained particle filter(EGCPF) is proposed. Using the epipolar geometry constraint

on one hand

the object detecting region is restricted during the tracking process to reduce the search space; on the other hand

the state transfer model of particle filtering algorithm is modified to decrease the total number of particles required for each camera with object in the overlap region. Finally

the frame rate

center error and coverage is introduced to measure the performance of the proposed algorithm. The experimental results showed that EGCPF algorithm met requirement in real-time performance

and the accuracy was improved obviously compared with the classical particle filter based object tracking algorithm.

关键词

Keywords

references

薛明. 基于稀疏表示的在线目标跟踪研究[D]. 上海: 上海交通大学, 2014.

Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering[J]. Statistics and computing, 2000, 10(3): 197-208. DOI: 197

Sanjeev A M, Maskell S, Gordon N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188. DOI: 174

应再恩, 李正洋, 平雪良, 等. 基于双目视觉动态跟踪的机器人标定[J]. 计算机应用研究, 2014, 31(5): 1424-1427. DOI: 1424

代江华. 粒子滤波架构下视觉目标跟踪相关技术研究[D]. 武汉: 华中科技大学, 2014.

Zisserman A. Multiple view geometry in computer vision[J]. Kybernetes, 2001, 30(9/10): 1865-1872. DOI: 1865

夏艳, 苏中, 吴细宝. 双目视觉测量系统的标定及3维测量[J]. 中国图象图形学报, 2008, 13(7): 1298-1302. DOI: 1298

狄红卫, 柴颖, 李逵. 一种快速双目视觉立体匹配算法[J]. 光学学报, 2009, 29(8): 2180-2184. DOI: 2180

Ross D A, Lim J, Lin R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1): 125-141. DOI: 125

杨丰瑞, 刘亭, 刘雄风. 基于粒子滤波和压缩感知的目标跟踪算法[J]. 电子技术应用, 2016, 42(7): 130-133. DOI: 130

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