Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (1): 268-277.doi: 10.13229/j.cnki.jdxbgxb20180801

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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

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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

CLC Number: 

  • TP391

Fig.1

Object tracking algorithm framework based on low?rank sparse representation under illumination change"

Fig.2

Image vectorization for illumination compensation"

Table 1

Video sequence and its main challenges"

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

Fig.3

Five algorithms track results at some times"

Table 2

Average center error and average overlap rate of different tracking methods"

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

Table 3

Impact of fast candidate target screening schemes on operating speed"

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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