结合稀疏表示的跨摄像头运动目标跟踪算法

doi:10.19665/j.issn1001-2400.2021.02.025

Abstract

Abstract:

Cross-camera target tracking is very challenging,mainly because of the difference in the background area under different cameras and the randomness of the target movement behavior trajectory,which will accumulate interference errors very easily,and affect the matching accuracy,thus leading to the tracking failure.Aiming at this problem,a model of moving target tracking based on sparse representation is proposed in this paper.The model uses the difference in background brightness between different cameras to compensate the illumination of the target,so as to obtain a stable template matrix.At the stage of model solution,to solve the problem that the traditional greedy algorithm has a single atom matching pattern,ignoring the relationship between inner atoms and leading to a low reconstruction accuracy,the model adopts the band exclusion(BE) method in the band exclusion local optimization orthogonal matching pursuit(BLOOMP) algorithm to reduce the interatomic coherence.In addition,combining the local optimization(LO) technique with the new coherence discrimination mechanism,we obtain a more compact correlation band to update the support set,leading to improving the reconstruction accuracy.At the stage of template updating,in order to enhance the real time performance of the template matrix,the model uses correlation band and different weight coefficients as the template replacement mechanism.Simulation results show that the proposed method can track the interested target stably and robustly compared with the traditional algorithm under the condition of indoor and outdoor scenes.

Key words: across cameras, sparse representation, illumination compensation, target tracking, template update

CLC Number:

TP391

LU Yan,LIAO Guisheng,HUANG Qingxiang. Cross-camera moving target tracking algorithm based on sparse representation[J].Journal of Xidian University, 2021, 48(2): 197-204.

Figures/Tables 10

References 18

[1]	王海军, 张圣燕. 自适应权值卷积特征的鲁棒目标跟踪算法[J]. 西安电子科技大学学报, 2019,46(1):117-123.
	WANG Haijun, ZHANG Shengyan. Robust Object Tracking via Adaptive Weighted Convolutional Features[J]. Journal of XIDIAN University, 2019,46(1):117-123.
[2]	王欣远, 肖嵩, 李磊, 等. 融合ELM和相关滤波的鲁棒性目标跟踪算法[J]. 西安电子科技大学学报, 2019,46(1):57-63.
	WANG Xinyuan, XIAO Song, LI Lei, et al. Robust Target Tracking Algorithm Based on the ELM and Discriminative Correlation Filter[J]. Journal of XIDIAN University, 2019,46(1):57-63.
[3]	CUI Y, ZHANG J, HE Z, et al. Multiple Pedestrian Tracking by Combining Particle Filter and Network Flow Model[J]. Neurocomputing, 2019,351:217-227.
[4]	CHEN X, BHANU B. Integrating Social Grouping for Multitarget Tracking Across Cameras in A CRF Model[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017,27(11):2382-2394.
[5]	CHEN W H, CAO L J, CHEN X T, et al. An Equalized Global Graph Model-Based Approach for Multicamera Object Tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017,27(11):2367-2381.
[6]	WANG X G. Intelligent Multi-camera Video Surveillance:A View[J]. Pattern Recognition Letters, 2013,34(1):3-19.
[7]	SOORI U, YUEN P, HAN J W, et al. Target Recognitions in Multiple-Camera Closed-Circuit Television Using Color Constancy[J]. Optical Engineering, 2013,52(4):602-614.
[8]	AL DELAIL B, BHASKAR H, ZEMERLY M J, et al. Robust Likelihood Model for Illumination Invariance in Particle Filtering[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018,28(10):2836-2848.
[9]	顾鑫, 费智婷. 基于局部背景加权直方图的目标跟踪[J]. 系统工程与电子技术, 2016,38(1):200-204.
	GU Xin, FEI Zhiting. Object Tracking Based on Local Background Weighted Histogram[J]. Systems Engineering and Electronics, 2016,38(1):200-204.
[10]	MEI X, LING H. Robust Visual Tracking Using L1 Minimization[C]//Proceedings of the 2009 12th IEEE International Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2009: 1436-1443.
[11]	王洪雁, 邱贺磊, 郑佳, 等. 光照变化下基于逆向稀疏表示的视觉跟踪方法[J]. 电子与信息学报, 2019,41(3):632-639.
	WANG Hongyan, QIU Helei, ZHENG Jia, et al. Visual Tracking Method Based on Reverse Sparse Representation under Illumination Variation[J]. Journal of Electronics & Information Technology, 2019,41(3):632-639.
[12]	TROPP J A, GILBERT A C. Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit[J]. IEEE Transactions on Information Theory, 2007,53(12):4655-4666. doi: 10.1109/TIT.2007.909108
[13]	DO T T, GAN L, NGUYEN N, et al. Sparsity Adaptive Matching Pursuit Algorithm for Practical Compressed Sensing[C]//Proceedings of the 2008 Asilomar Conference on Signals,Systems and Computers. Washington:IEEE Computer Society, 2008: 581-587.
[14]	吕伟杰, 孟博, 张飞. 基于压缩感知的稀疏度自适应匹配追踪改进算法[J]. 控制与决策, 2018,33(9):1657-1661.
	LYU Weijie, MENG Bo, ZHANG Fei. Modified Sparsity Adaptive Matching Pursuit Algorithm Based on Compressive Sensing[J]. Control and Decision, 2018,33(9):1657-1661.
[15]	JU J, XING J. Moving Object Detection Based on Smoothing Three Frame Difference Method Fused with RPCA[J]. Multimedia Tools and Applications, 2019,78(21):29937-29951. doi: 10.1007/s11042-018-6710-1
[16]	FANNJIANG A, LIAO W. Super-resolution by Compressive Sensing Algorithms[C]//Proceedings of the 2012 Asilomar Conference on Signals,Systems and Computers. Washington:IEEE Computer Society, 2012: 411-415.
[17]	郁道银, 王悦行, 陈晓冬, 等. 基于随机投影和稀疏表示的跟踪算法[J]. 电子与信息学报, 2016,38(7):1602-1608. doi: 10.11999/JEIT151064
	YU Daoyin, WANG Yuexing, CHEN Xiaodong, et al. Visual Tracking Based on Random Projection and Sparse Representation[J]. Journal of Electronics & Information Technology, 2016,38(7):1602-1608. doi: 10.11999/JEIT151064
[18]	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: 10.1007/s11263-007-0075-7

实验	IVT算法		OMP算法		文中算法
实验1-1	0.9	0.4	0.8	0.9	0.1	0.2
	21.2	76.1	137.1	31.2	11.1	9.1
	12.7	46.3	74.2	17.9	4.05	4.31
实验1-2	1.0	0.8	0.7	0.6	1.1	1.2
	27.1	48.4	43.8	75.6	10.0	11.0
	8.27	23.1	15.7	46.0	5.96	4.93
实验2	0.8	0.9	0.6	0.8	0.2	0.1
	61.2	50.2	158.9	147.9	19.7	10.1
	33.9	32.9	52.3	90.1	8.55	4.89

实验	IVT算法	OMP算法	文中算法
实验1-1	0.02	0	0.55
	0.94	0.92	0.94
	0.16	0.12	0.76
实验1-2	0.06	0	0.61
	0.90	0.89	0.92
	0.33	0.13	0.74
实验2	0.17	0	0.65
	0.91	0.91	0.93
	0.38	0.31	0.79

Cross-camera moving target tracking algorithm based on sparse representation

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 18

Related Articles 15

Metrics

Comments

Recommended 10

[1]	GU Zhaojun,CHEN Hui,WANG Jialiang,GAO Bing. Target tracking control algorithm for small size quad-rotor helicopter [J]. Journal of Xidian University, 2021, 48(5): 117-127.
[2]	ZHANG Yichen,SHUI Penglang. Improved probabilistic multi-hypothesis tracker via the Poisson point process [J]. Journal of Xidian University, 2021, 48(2): 27-34.
[3]	LI Cuiyun,LI Yang,JI Hongbing,SHI Renzheng. GLMB extended target tracking based on one-step data association [J]. Journal of Xidian University, 2020, 47(5): 137-143.
[4]	ZHAO Hui,ZHANG Le,LIU Yingli,ZHANG Jing,ZHANG Tianqi. Optimization of the low coherence and high robustness observation matrix [J]. Journal of Xidian University, 2019, 46(6): 171-178.
[5]	ZHANG Yunpu,SHAN Ganlin,DUAN Xiusheng,WANG Meng. Scheduling of active/passive sensors for radiation control [J]. Journal of Xidian University, 2019, 46(6): 67-74.
[6]	HE Wangpeng,HU Jie,CHEN Binqiang,LI Cheng,GUO Baolong. Sparsity-induced resonance demodulation method for blade crack detection [J]. Journal of Xidian University, 2019, 46(6): 75-80.
[7]	SONG Jianfeng,MIAO Qiguang,SHEN Meng,QUAN Yining,CHEN Yusheng. Algorithm for tracking an infrared single target based on correlation filtering with multi-feature fusion [J]. Journal of Xidian University, 2019, 46(5): 142-147.
[8]	YANG Lei,YUE Yunze,LI Pucheng,ZHANG Tao,YANG Huan. Sparse representation of large dynamic range SAR imaging for multiple ground moving targets [J]. Journal of Xidian University, 2019, 46(5): 31-40.
[9]	WANG Mingjie,JI Hongbing,LIU Long. Student’s t Distribution mixture CPHD filter with noise outliers [J]. Journal of Xidian University, 2019, 46(5): 134-141.
[10]	WANG Hongyan,QIU Helei,PEI Tengda. Visual tracking method using discriminant dictionary learning [J]. Journal of Xidian University, 2019, 46(4): 150-158.
[11]	JIA Hairong,WANG Weimei,WANG Yan,PEI Junhua. Speech enhancement based on discriminative joint sparse dictionary alternate optimization [J]. Journal of Xidian University, 2019, 46(3): 74-81.
[12]	LI Cuiyun,CHEN Dongwei,SHI Renzheng. Adaptive target birth δ-generalized labeled multi-Bernoulli filtering algorithm [J]. Journal of Xidian University, 2019, 46(2): 12-16.
[13]	YU Lihong,ZHAO Jiaxiang. Design of sparse MIMO equalizers using least angle regression [J]. Journal of Xidian University, 2019, 46(1): 73-78.
[14]	WANG Chuanchuan,ZENG Yonghu,FU Weihong,WANG Liandong. Estimation algorithm for an underdetermined mixing matrix based on maximum density point searching [J]. Journal of Xidian University, 2019, 46(1): 106-111.
[15]	LI Long;LIU Zheng. Noise-robust multi-feature joint learning HRRP recognition method [J]. Journal of Xidian University, 2018, 45(4): 57-62.