光照变化下基于低秩稀疏表示的视觉跟踪方法

doi:10.13229/j.cnki.jdxbgxb20180801

摘要/Abstract

摘要：

针对光照变化引起目标跟踪性能显著下降的问题，提出一种光照补偿和低秩稀疏表示联合优化的视觉跟踪方法。该方法首先基于模板与候选目标的平均亮度差异对模板光照补偿；而后利用候选目标逆向稀疏表示模板，并考虑候选目标间相似性以对稀疏编码矩阵实施低秩约束，且包含稀疏误差项以提高算法对局部遮挡的稳健性，从而获得光照补偿和低秩稀疏表示联合优化模型；最后，基于该模型所得稀疏编码矩阵快速剔除无关候选目标，并采用局部结构化评估方法实现目标精确跟踪。仿真结果表明，与现有主流算法相比，剧烈光照变化情况下，本文方法可显著改善目标跟踪精度及鲁棒性。

关键词: 计算机应用, 视觉跟踪, 光照补偿, 稀疏表示, 贝叶斯推理

Abstract:

To solve the problem of heavy decrease in object tracking performance induced by illumination change, a visual tracking method via jointly optimizing the illumination compensation and low-rank sparse representation is proposed. The template illumination is firstly compensated by the developed algorithm, which is based on the average brightness difference between templates and candidates. Second, the candidate set is exploited to sparsely represent templates after illumination compensation, afterwards the similarity between the candidates is considered to implement low rank constraint on the sparse coding matrix, and the sparse error term is included to improve the robustness of the algorithm to local occlusion, thereby an illumination compensation and low rank sparse representation joint optimization model can be constructed. Finally, the sparse coding matrix obtained by solving the model is used to quickly eliminate the irrelevant candidates, and then the local structured evaluation method is employed to achieve the object tracking with high accuracy. As compared to the existing state-of-the-art algorithms, simulation results show that the proposed algorithm can improve the accuracy and robustness of the object tracking significantly in the presence of heavy illumination change.

Key words: computer application, visual tracking, illumination compensation, sparse representation, Bayesian inference

中图分类号:

TP391

王洪雁,邱贺磊,郑佳,裴炳南. 光照变化下基于低秩稀疏表示的视觉跟踪方法[J]. 吉林大学学报(工学版), 2020, 50(1): 268-277.

Hong-yan WANG,He-lei QIU,Jia ZHENG,Bing-nan PEI. Visual tracking method based on low⁃rank sparse representation under illumination change[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 268-277.

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视频序列	挑战因素
CarDark	光照变化，背景杂波
Mhyang	光照变化，变形，像平面外旋转等
Car4	光照变化，尺度变化
FaceOcc2	光照变化，遮挡，像平面内旋转等
Car2	光照变化，尺度变化，运动模糊等
Singer1	光照变化，尺度变化，遮挡等

测试序列	平均中心误差					平均重叠率
测试序列	CT	TLD	MIL	LSK	本文	CT	TLD	MIL	LSK	本文
平均	45.94	12.22	33.41	33.34	3.11	0.35	0.63	0.36	0.53	0.84
CarDark	99.22	27.47	43.48	1.29	1.28	0.11	0.45	0.20	0.84	0.85
Mhyang	13.28	9.51	20.40	3.03	3.06	0.60	0.63	0.51	0.84	0.80
Car4	86.03	12.84	50.78	66.59	3.26	0.21	0.63	0.26	0.15	0.86
FaceOcc2	18.95	12.28	13.60	14.57	6.59	0.61	0.62	0.67	0.63	0.77
Car2	42.64	3.23	55.81	93.86	1.70	0.23	0.72	0.17	0.39	0.91
Singer1	15.53	7.99	16.37	20.72	2.98	0.35	0.73	0.36	0.34	0.86

序列	不采用筛选方案/(帧·s^-1)	采用筛选方案/(帧·s^-1)
平均	4.3	9.6
CarDark	5.3	8.7
Mhyang	4.5	9.4
Car4	4.1	10.5
FaceOcc2	3.9	10.1
Car2	4.3	9.9
Singer1	4.6	8.7