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

摘要/Abstract

摘要：

跨摄像头下的目标跟踪极富挑战性,其原因是由于不同摄像头所涵盖区域存在差异性以及运动目标行为轨迹具有随机性,从而导致干扰误差的积累,影响匹配准确度,致使跟踪失败。针对此问题,提出一种结合稀疏表示理论的跟踪模型。该模型首先通过不同摄像头间的背景亮度值,对待测目标进行光照补偿处理,以获取稳定的模板矩阵。在模型求解阶段,针对传统贪婪算法原子匹配模式单一、易忽略原子内在联系、重构精度低的问题,利用带宽排除局部优化正交匹配追踪算法中的带排除方法降低原子间相干性的影响;将局部优化技术与新的相干性判别机制结合,以此获得更为紧凑的相关带来更新支撑集,从而提高重构精度。在模板更新阶段,采用一种以相关带为单位,并根据不同的权重系数进行判断的模板替换机制,以加强模板矩阵的抗干扰性。仿真结果表明,所提方法相较于传统算法在室内及室外场景中均能稳定、鲁棒地跟踪到感兴趣的目标。

关键词: 跨摄像头, 稀疏表示, 光照补偿, 目标跟踪, 模板更新

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

中图分类号:

TP391

逯彦,廖桂生,黄庆享. 结合稀疏表示的跨摄像头运动目标跟踪算法[J]. 西安电子科技大学学报, 2021, 48(2): 197-204.

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.

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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