Please wait a minute...
Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2019, Vol. 13 Issue (1) : 199-211    https://doi.org/10.1007/s11704-017-6434-9
RESEARCH ARTICLE
Visual tracking using discriminative representation with 2 regularization
Haijun WANG1,2(), Hongjuan GE1
1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2. Aviation Information Technology Research and Development Center, Binzhou University, Binzhou 256603, China
 Download: PDF(1639 KB)  
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

In this paper, we propose a novel visual tracking method using a discriminative representation under a Bayesian framework. First, we exploit the histogram of gradient (HOG) to generate the texture features of the target templates and candidates. Second, we introduce a novel discriminative representation and 2-regularized least squares method to solve the proposed representation model. The proposed model has a closed-form solution and very high computational efficiency. Third, a novel likelihood function and an update scheme considering the occlusion factor are adopted to improve the tracking performance of our proposed method. Both qualitative and quantitative evaluations on 15 challenging video sequences demonstrate that our method can achieve more robust tracking results in terms of the overlap rate and center location error.

Keywords visual tracking      discriminative representation      Bayesian framework      closed-form solution     
Corresponding Author(s): Haijun WANG   
Just Accepted Date: 20 January 2017   Online First Date: 06 March 2018    Issue Date: 31 January 2019
 Cite this article:   
Haijun WANG,Hongjuan GE. Visual tracking using discriminative representation with 2 regularization[J]. Front. Comput. Sci., 2019, 13(1): 199-211.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-017-6434-9
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I1/199
1 ALi, MLin, YWu, M HYang, S CYan. NUS-PRO: a new visual tracking challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 335–349
https://doi.org/10.1109/TPAMI.2015.2417577
2 YWu, JLim, M HYang. Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834–1848
https://doi.org/10.1109/TPAMI.2014.2388226
3 K HZhang, LZhang, M HYang. Fast compressive tracking. IEEE Transations on Pattern Analysis and Machine Intelligence, 2014, 36(10): 2002–2015
https://doi.org/10.1109/TPAMI.2014.2315808
4 XLi, C HShen, ADick, A Hengel. Learning compact binary codes for visual tracking. In: Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2419–2426
https://doi.org/10.1109/CVPR.2013.313
5 K HZhang, LZhang, M HYang, Q H Hu. Robust object tracking via active feature selection. IEEE Transactions Circuits and Systems for Video Technology, 2013, 23(11): 1957–1967
https://doi.org/10.1109/TCSVT.2013.2269772
6 H HSong. Robust visual tracking via online informative feature selection. Electronics Letters, 2014, 50(25): 1931–1933.
https://doi.org/10.1049/el.2014.1911
7 BBabenko, M HYang, SBelongie. Visual tracking with online multiple instance learning. In: Proceedings of the 22nd IEEE Conference on Computer Vision and Pattern Recognition. 2009, 983–990
8 K HZhang, Q SLiu, YWu, M HYang. Robust visual tracking via convolutional networks without training. IEEE Transations on Image Processing, 2016, 25(4): 1779–1792
https://doi.org/10.1109/TIP.2016.2531283
9 JYan, XChen, D XDeng, Q P Zhu. Visual object tracking via online sparse instance learning. Journal of Visual Communication and Image Representation, 2015, 26: 231–246
https://doi.org/10.1016/j.jvcir.2014.11.013
10 K HZhang, LZhang, M HYang. Real-time object tracking via online discriminative feature selection. IEEE Transactions on Image Processing, 2013, 22(12): 4664–4677
https://doi.org/10.1109/TIP.2013.2277800
11 H HSong, Y HZheng, K HZhang. Robust visual tracking via selfsimilarity learning. Electronics Letters, 2017, 53(1): 20–22
https://doi.org/10.1049/el.2016.3011
12 XYang, MWang, L MZhang, F M Sun, R CHong, M BQi. An efficient tracking system by orthogonalized templates. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3187–3197
https://doi.org/10.1109/TIE.2016.2515559
13 DWang, H CLu, Z YXiao, M H Yang. Inverse sparse tracker with a locally weighted distance metric. IEEE Transactions on Image Processing, 2015, 24(9): 2646–2657
https://doi.org/10.1109/TIP.2015.2427518
14 DWang, H CLu. Online visual tracking via two view sparse representation. IEEE Signal Processing Letters, 2014, 21(9): 1031–1034
https://doi.org/10.1109/LSP.2014.2322389
15 Y HHan, YYang, YYan, Z G Ma, NSebe, X FZhou. Semisupervised feature selection via spline regression for video semantic recognition. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(2): 252–264
https://doi.org/10.1109/TNNLS.2014.2314123
16 Y HHan, FWu, QTian, Y T Zhuang. Image annotation by input-output structural grouping sparsity. IEEE Transactions on Image Processing, 2012, 21(6): 3066–3079
https://doi.org/10.1109/TIP.2012.2183880
17 JYang, D LChu, LZhang, Y Xu, J YYang. Sparse representation classifier steered discriminative projection with applications to face recognition. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(7): 1023–1035
https://doi.org/10.1109/TNNLS.2013.2249088
18 JWright, A YYang, AGanesh, S Sastry, YMa. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210–227
https://doi.org/10.1109/TPAMI.2008.79
19 B HZhuang, H CLu, Z YXiao, D Wang. Visual tracking via discriminative sparse similarity map. IEEE Transactions on Image Processing, 2014, 23(4): 1872–1881
https://doi.org/10.1109/TIP.2014.2308414
20 H WHu, BMa, Y DJia. Multi-task L0 gradient minimization for visual tracking. Neurocomputing, 2015, 154(22): 41–49
https://doi.org/10.1016/j.neucom.2014.12.021
21 J HYoon, M HYang, K JYoon. Interacting multiview tracker. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(5): 903–917
https://doi.org/10.1109/TPAMI.2015.2473862
22 J SPan, JLim, Z XSu, M H Yang. L0-regularized object representation for visual tracking. In: Proceedings of the British Machine Vision Conference. 2014, 1–12
https://doi.org/10.5244/C.28.29
23 BMa, J BShen, Y BLiu, H W Hu, LShao, X LLi. Visual tracking using strong classifier and structural local sparse descriptors. IEEE Transactions on Multimedia, 2015, 17(10): 1818–1828
https://doi.org/10.1109/TMM.2015.2463221
24 XMei, H BLing. Robust visual tracking using l1 minimization. In: Proceedings of the 12th IEEE International Conference on Computer Vision. 2009, 1436–1443
25 C LBao, YWu, H BLing, H Ji. Real time robust l1 tracker using accelerated proximal gradient approach. In: Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1830–1837
26 XJia, H CLu, M HYang. Visual tracking via coarse and fine structural local sparse appearance models. IEEE Transactions on Image Processing, 2016, 25(10): 4555–4564
https://doi.org/10.1109/TIP.2016.2592701
27 WZhong, H CLu, M HYang. Robust object tracking via sparse collaborative appearance model. IEEE Transactions on Image Processing, 2014, 23(5): 2356–2368
https://doi.org/10.1109/TIP.2014.2313227
28 DWang, H CLu, M HYang. Least soft-threshold squares tracking. In: Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2371–2378
https://doi.org/10.1109/CVPR.2013.307
29 Y WWu, J SYuan, P YTan, Y D Jia, JZhang. Robust distracterresistive tracker via learning a multi-component discriminative dictionary. IEEE Transactions on Image Processing, submitted.
30 DWang, H CLu, M HYang. Kernel collaborative face recognition. Pattern Recognition, 2015, 48(10): 3025–3237
https://doi.org/10.1016/j.patcog.2015.01.012
31 LZhang, M HYang, X CFeng. Sparse representation or collaborative representation: Which helps face recognition? In: Proceedings of the 13th IEEE International Conference on Computer Vision. 2011, 471–478
https://doi.org/10.1109/ICCV.2011.6126277
32 S JCai, LZhang, W MZuo, X C Feng. A probabilistic collaborative representation based approach for pattern classification. In: Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. 2016, 2950–2959
https://doi.org/10.1109/CVPR.2016.322
33 S FShi, A Eriksson, AHengel, C HShen. Is face recognition really a compressive sensing problem? In: Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition. 2011, 553–560
https://doi.org/10.1109/CVPR.2011.5995556
34 Z YXiao, H CLu, DWang. L2-RLS based object tracking. IEEE Transaction on Circuits and Systems for Video Technology, 2014, 24(8): 1301–1308
https://doi.org/10.1109/TCSVT.2013.2291355
35 NDalal, BTriggs. Histograms of oriented gradients for human detection. In: Proceedings of the 18th IEEE Conference on Computer Vision and Pattern Recognition. 2005, 886–893
https://doi.org/10.1109/CVPR.2005.177
36 JHenriques, R Caseiro, PMartins, JBatista. Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the 12th European Conference on Computer Vision. 2012, 702–715
https://doi.org/10.1007/978-3-642-33765-9_50
37 YXu, Z FZhong, JYang, J You, DZhang. A new discriminative sparse representation method for robust face recognition via l2 regularization. IEEE Transactions on Neural Networks and Learning Systems, 2016, PP(99): 1–10
38 XJia, H CLu, M HYang. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1822–1829
39 DWang, H CLu. On-line learning parts-based representation via incremental orthogonal projective non-negative matrix factorization. Signal Processing, 2013, 93(6): 1608–1623
https://doi.org/10.1016/j.sigpro.2012.07.015
40 DWang, H CLu, M HYang. Online object tracking with sparse prototypes. IEEE Transactions on Image Processing, 2013, 22(1): 314–325
https://doi.org/10.1109/TIP.2012.2202677
41 DWang, H CLu. Visual tracking via probability continuous outlier model. In: Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition. 2014, 3478–3485
https://doi.org/10.1109/CVPR.2014.445
42 AAdam, ERivlin, IShimshoni. Robust fragments-based tracking using the integral histogram. In: Proceedings of the 19th IEEE Conference on Computer Vision and Pattern Recognition. 2006, 798–805
https://doi.org/10.1109/CVPR.2006.256
43 J SKwon, K MLee. Visual tracking decomposition. In: Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1269–1276
https://doi.org/10.1109/CVPR.2010.5539821
[1] Zhenyang SU, Jing LI, Jun CHANG, Bo DU, Yafu XIAO. Real-time visual tracking using complementary kernel support correlation filters[J]. Front. Comput. Sci., 2020, 14(2): 417-429.
[2] Jiaqing FAN, Huihui SONG, Kaihua ZHANG, Qingshan LIU, Fei YAN, Wei LIAN. Real-time manifold regularized context-aware correlation tracking[J]. Front. Comput. Sci., 2020, 14(2): 334-348.
[3] Jun ZHANG, Bineng ZHONG, Pengfei WANG, Cheng WANG, Jixiang DU. Robust feature learning for online discriminative tracking without large-scale pre-training[J]. Front. Comput. Sci., 2018, 12(6): 1160-1172.
[4] Nan REN,Junping DU,Suguo ZHU,Linghui LI,Dan FAN,JangMyung LEE. Robust visual tracking based on scale invariance and deep learning[J]. Front. Comput. Sci., 2017, 11(2): 230-242.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed