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

Previous Articles    

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

RICH HTML

 5   

PDF (PC)

316

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
1 Fradi H, Luvison B, Pham Q C. Crowd behavior analysis using local mid-level visual descriptors[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(3): 589-602.
2 Yu G, Li C, Shang Z Y. Video monitoring method, video monitoring system and computer program product[P]. US:9792505, 2017-10-17.
3 Ueng S K, Chen G Z. Vision based multi-user human computer interaction[J]. Multimedia Tools and Applications, 2016, 75(16): 10059-10076.
4 Wu Y, Lim J, Yang M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848.
5 Pan Z, Liu S, Fu W. A review of visual moving target tracking[J]. Multimedia Tools and Applications, 2017, 76(16): 16989-17018.
6 李根, 李文辉. 基于思维进化算法的人脸特征点跟踪[J]. 吉林大学学报:工学版, 2015, 45(2): 606-612.
Li Gen, Li Wen-hui. Facial feature tracking based on mind evolutionary algorithm[J]. Journal of Jilin University (Engineering and Technology Edition), 2015, 45(2): 606-612.
7 才华, 陈广秋, 刘广文, 等. 遮挡环境下多示例学习分块目标跟踪[J]. 吉林大学学报:工学版, 2017, 47(1): 281-287.
Cai Hua, Chen Guang-qiu, Liu Guang-wen, et al. Novelty fragments-based target tracking with multiple instance learning under occlusions[J]. Journal of Jilin University (Engineering and Technology Edition), 2017, 47(1): 281-287.
8 Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
9 Zhang J, Zhao D, Gao W. Group-based sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2014, 23(8): 3336-3351.
10 Mei X, Ling H. Robust visual tracking using ℓ1 minimization[C]∥IEEE 12th International Conference on Computer Vision, Kyoto, Japan, 2009: 1436-1443.
11 Xiao Z, Lu H, Wang D. Object tracking with L2-RLS[C]∥IEEE International Conference on Pattern Recognition, Tsukuba, Japan, 2012: 1351-1354.
12 Zhang T, Liu S, Ahuja N, et al. Robust visual tracking via consistent low-rank sparse learning[J]. International Journal of Computer Vision, 2015, 111(2): 171-190.
13 He Z, Yi S, Cheung Y M, et al. Robust object tracking via key patch sparse representation[J]. IEEE Transactions on Cybernetics, 2017, 47(2): 354-364.
14 Silveira G, Malis E. Real-time visual tracking under arbitrary illumination changes[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 2007: 1-6.
15 Nhat V Q, Lee G. Illumination invariant object tracking with adaptive sparse representation[J]. International Journal of Control Automation and Systems, 2014, 12(1): 195-201.
16 Jenkins M D, Barrie P, Buggy T, et al. Selective sampling importance resampling particle filter tracking with multibag subspace restoration[J]. IEEE Transactions on Cybernetics, 2018, 48(1): 264-276.
17 Delail B A, 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.
18 Sui Y, Zhang L. Robust tracking via locally structured representation[J]. International Journal of Computer Vision, 2016, 119(2): 110-144.
19 Zhang K, Zhang L, Yang M H. Real-time compressive tracking[C]∥European Conference on Computer Vision, Florence, Italy, 2012: 864-877.
20 Peng Y, Ganesh A, Wright J, et al. RASL: robust alignment by sparse and low-rank decomposition for linearly correlated images[J]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010: 763-770.
21 Polson N, Sokolov V. Bayesian particle tracking of traffic flows[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(2): 345-356.
22 Ross D A, Lim J, Lin R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1-3): 125-141.
23 Jia X, Lu H, Yang M H. Visual tracking via coarse and fine structural local sparse appearance models[J]. IEEE Transactions on Image Processing, 2016, 25(10): 4555-4564.
24 Kalal Z, Matas J, Mikolajczyk K. P-N learning: Bootstrapping binary classifiers by structural constraints[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010: 49-56.
25 Babenko B, Yang M H, Belongie S. Visual tracking with online multiple instance learning[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 983-990.
26 Liu B, Huang J, Yang L, et al. Robust tracking using local sparse appearance model and K-selection[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2011: 1313-1320.
[1] Xiao-dong ZHANG,Xiao-jun XIA,Hai-feng LYU,Xu-chao GONG,Meng-jia LIAN. Dynamic load balancing of physiological data flow in big data network parallel computing environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 247-254.
[2] Jun-yi DENG,Yan-heng LIU,Shi FENG,Rong-cun ZHAO,Jian WANG. GSPN⁃based model to evaluate the performance and securi tytradeoff in Ad-hoc network [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 255-261.
[3] Xiong-fei LI,Jing WANG,Xiao-li ZHANG,Tie-hu FAN. Multi-focus image fusion based on support vector machines and window gradient [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 227-236.
[4] Bing-hai ZHOU,Qiong WU. Balancing and optimization of robotic assemble lines withtool and space constraint [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2069-2075.
[5] Xiang-jiu CHE,Hua-luo LIU,Qing-bin SHAO. Fabric defect recognition algorithm based onimproved Fast RCNN [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2038-2044.
[6] Hong-wei ZHAO,Peng WANG,Li-li FAN,Huang-shui HU,Ping-ping LIU. Similarity retention instance retrieval method [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2045-2050.
[7] Jun SHEN,Xiao ZHOU,Zu-qin JI. Implementation of service dynamic extended network and its node system model [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2058-2068.
[8] You ZHOU,Sen YANG,Da-lin LI,Chun-guo WU,Yan WANG,Kang-ping WANG. Acceleration platform for face detection and recognition based on field⁃programmable gate array [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2051-2057.
[9] Bin LI,Xu ZHOU,Fang MEI,Shuai-ning PAN. Location recommendation algorithm based on K-means and matrix factorization [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1653-1660.
[10] Xiong-fei LI,Lu SONG,Xiao-li ZHANG. Remote sensing image fusion based on cooperative empirical wavelet transform [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1307-1319.
[11] Yuan-ning LIU,Shuai LIU,Xiao-dong ZHU,Guang HUO,Tong DING,Kuo ZHANG,Xue JIANG,Shu-jun GUO,Qi-xian ZHANG. Iris secondary recognition based on decision particle swarm optimization and stable texture [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1329-1338.
[12] Bin LI,Guo⁃jun SHEN,Geng SUN,Ting⁃ting ZHENG. Improved chicken swarm optimization algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1339-1344.
[13] Feng⁃wen ZHAI,Jian⁃wu DANG,Yang⁃ping WANG,Jing JIN,Wei⁃wei LUO. Extended contour⁃based fast affine invariant feature extracting [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1345-1356.
[14] Yan-jun SUN,Xuan-jing SHEN,Hai-peng CHEN,Yong-zhe ZHAO. Recaptured image forensics algorithm based on local plane linear point [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1320-1328.
[15] Nan WANG,Jin⁃bao LI,Yong LIU,Yu⁃jie ZHANG,Ying⁃li ZHONG. TPR⁃TF： time⁃aware point of interest recommendation model based on tensor factorization [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 920-933.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd