吉林大学学报(工学版) ›› 2020, Vol. 50 ›› Issue (1): 268-277.doi: 10.13229/j.cnki.jdxbgxb20180801

• 计算机科学与技术 • 上一篇    

光照变化下基于低秩稀疏表示的视觉跟踪方法

王洪雁1(),邱贺磊1,郑佳1,裴炳南2   

  1. 1. 大连大学 信息工程学院,辽宁 大连 116622
    2. 泉州信息工程学院 电子与通信工程学院,福建 泉州362000
  • 收稿日期:2018-07-30 出版日期:2020-01-01 发布日期:2020-02-06
  • 作者简介:王洪雁(1979-),男,副教授,博士.研究方向:MIMO雷达信号处理,毫米波通信,机器视觉.E-mail: gglongs@163.com
  • 基金资助:
    国家自然科学基金项目(61301258);中国博士后科学基金项目(2016M590218)

Visual tracking method based on low⁃rank sparse representation under illumination change

Hong-yan WANG1(),He-lei QIU1,Jia ZHENG1,Bing-nan PEI2   

  1. 1. College of Information Engineering, Dalian University, Dalian 116622, China
    2. College of Electronic and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou 362000, China
  • Received:2018-07-30 Online:2020-01-01 Published:2020-02-06

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

图1

光照变化下基于低秩稀疏表示的目标跟踪算法框架"

图2

用于光照补偿的图像矢量化"

表1

视频序列及其主要挑战因素"

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

图3

部分时刻5种算法跟踪结果"

表2

不同跟踪方法的平均中心误差和平均重叠率"

测试序列平均中心误差平均重叠率
CTTLDMILLSK本文CTTLDMILLSK本文
平均45.9412.2233.4133.343.110.350.630.360.530.84
CarDark99.2227.4743.481.291.280.110.450.200.840.85
Mhyang13.289.5120.403.033.060.600.630.510.840.80
Car486.0312.8450.7866.593.260.210.630.260.150.86
FaceOcc218.9512.2813.6014.576.590.610.620.670.630.77
Car242.643.2355.8193.861.700.230.720.170.390.91
Singer115.537.9916.3720.722.980.350.730.360.340.86

表3

快速候选目标筛选方案对运行速度的影响"

序 列不采用筛选方案/(帧·s-1)采用筛选方案/(帧·s-1)
平均4.39.6
CarDark5.38.7
Mhyang4.59.4
Car44.110.5
FaceOcc23.910.1
Car24.39.9
Singer14.68.7
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