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 ℓ2-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.
Similar content being viewed by others
References
Li A, Lin M, Wu Y, Yang M H, Yan S C. NUS-PRO: a new visual tracking challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 335–349
Wu Y, Lim J, Yang M H. Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834–1848
Zhang K H, Zhang L, Yang M H. Fast compressive tracking. IEEE Transations on Pattern Analysis and Machine Intelligence, 2014, 36(10): 2002–2015
Li X, Shen C H, Dick A, Hengel A. Learning compact binary codes for visual tracking. In: Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2419–2426
Zhang K H, Zhang L, Yang M H, Hu Q H. Robust object tracking via active feature selection. IEEE Transactions Circuits and Systems for Video Technology, 2013, 23(11): 1957–1967
Song H H. Robust visual tracking via online informative feature selection. Electronics Letters, 2014, 50(25): 1931–1933.
Babenko B, Yang M H, Belongie S. Visual tracking with online multiple instance learning. In: Proceedings of the 22nd IEEE Conference on Computer Vision and Pattern Recognition. 2009, 983–990
Zhang K H, Liu Q S, Wu Y, Yang M H. Robust visual tracking via convolutional networks without training. IEEE Transations on Image Processing, 2016, 25(4): 1779–1792
Yan J, Chen X, Deng D X, Zhu Q P. Visual object tracking via online sparse instance learning. Journal of Visual Communication and Image Representation, 2015, 26: 231–246
Zhang K H, Zhang L, Yang M H. Real-time object tracking via online discriminative feature selection. IEEE Transactions on Image Processing, 2013, 22(12): 4664–4677
Song H H, Zheng Y H, Zhang K H. Robust visual tracking via self-similarity learning. Electronics Letters, 2017, 53(1): 20–22
Yang X, Wang M, Zhang L M, Sun F M, Hong R C, Qi M B. An efficient tracking system by orthogonalized templates. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3187–3197
Wang D, Lu H C, Xiao Z Y, Yang M H. Inverse sparse tracker with a locally weighted distance metric. IEEE Transactions on Image Processing, 2015, 24(9): 2646–2657
Wang D, Lu H C. Online visual tracking via two view sparse representation. IEEE Signal Processing Letters, 2014, 21(9): 1031–1034
Han Y H, Yang Y, Yan Y, Ma Z G, Sebe N, Zhou X F. Semisupervised feature selection via spline regression for video semantic recognition. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(2): 252–264
Han Y H, Wu F, Tian Q, Zhuang Y T. Image annotation by input-output structural grouping sparsity. IEEE Transactions on Image Processing, 2012, 21(6): 3066–3079
Yang J, Chu D L, Zhang L, Xu Y, Yang J Y. Sparse representation classifier steered discriminative projection with applications to face recognition. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(7): 1023–1035
Wright J, Yang A Y, Ganesh A, Sastry S, Ma Y. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210–227
Zhuang B H, Lu H C, Xiao Z Y, Wang D. Visual tracking via discriminative sparse similarity map. IEEE Transactions on Image Processing, 2014, 23(4): 1872–1881
Hu HW, Ma B, Jia Y D. Multi-task L0 gradient minimization for visual tracking. Neurocomputing, 2015, 154(22): 41–49
Yoon J H, Yang M H, Yoon K J. Interacting multiview tracker. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(5): 903–917
Pan J S, Lim J, Su Z X, Yang M H. L0-regularized object representation for visual tracking. In: Proceedings of the British Machine Vision Conference. 2014, 1–12
Ma B, Shen J B, Liu Y B, Hu H W, Shao L, Li X L. Visual tracking using strong classifier and structural local sparse descriptors. IEEE Transactions on Multimedia, 2015, 17(10): 1818–1828
Mei X, Ling H B. Robust visual tracking using 1 minimization. In: Proceedings of the 12th IEEE International Conference on Computer Vision. 2009, 1436–1443
Bao C L, Wu Y, Ling H B, Ji H. Real time robust 1 tracker using accelerated proximal gradient approach. In: Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1830–1837
Jia X, Lu H C, Yang M H. Visual tracking via coarse and fine structural local sparse appearance models. IEEE Transactions on Image Processing, 2016, 25(10): 4555–4564
Zhong W, Lu H C, Yang M H. Robust object tracking via sparse collaborative appearance model. IEEE Transactions on Image Processing, 2014, 23(5): 2356–2368
Wang D, Lu H C, Yang M H. Least soft-threshold squares tracking. In: Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2371–2378
Wu Y W, Yuan J S, Tan P Y, Jia Y D, Zhang J. Robust distracter-resistive tracker via learning a multi-component discriminative dictionary. IEEE Transactions on Image Processing, submitted.
Wang D, Lu H C, Yang M H. Kernel collaborative face recognition. Pattern Recognition, 2015, 48(10): 3025–3237
Zhang L, Yang M H, Feng X C. Sparse representation or collaborative representation: Which helps face recognition? In: Proceedings of the 13th IEEE International Conference on Computer Vision. 2011, 471–478
Cai S J, Zhang L, Zuo W M, Feng X C. 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
Shi S F, Eriksson A, Hengel A, Shen C H. Is face recognition really a compressive sensing problem? In: Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition. 2011, 553–560
Xiao Z Y, Lu H C, Wang D. L2-RLS based object tracking. IEEE Transaction on Circuits and Systems for Video Technology, 2014, 24(8): 1301–1308
Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the 18th IEEE Conference on Computer Vision and Pattern Recognition. 2005, 886–893
Henriques J, Caseiro R, Martins P, Batista J. Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the 12th European Conference on Computer Vision. 2012, 702–715
Xu Y, Zhong Z F, Yang J, You J, Zhang D. A new discriminative sparse representation method for robust face recognition via 2 regularization. IEEE Transactions on Neural Networks and Learning Systems, 2016, PP(99): 1–10
Jia X, Lu H C, Yang M H. 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
Wang D, Lu H C. On-line learning parts-based representation via incremental orthogonal projective non-negative matrix factorization. Signal Processing, 2013, 93(6): 1608–1623
Wang D, Lu H C, Yang M H. Online object tracking with sparse prototypes. IEEE Transactions on Image Processing, 2013, 22(1): 314–325
Wang D, Lu H C. Visual tracking via probability continuous outlier model. In: Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition. 2014, 3478–3485
Adam A, Rivlin E, Shimshoni I. Robust fragments-based tracking using the integral histogram. In: Proceedings of the 19th IEEE Conference on Computer Vision and Pattern Recognition. 2006, 798–805
Kwon J S, Lee K M. Visual tracking decomposition. In: Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1269–1276
Acknowledgements
This work was supported by a project of Shandong Province Higher Educational Science and Technology Program (J17KA088 and J16LN02), the Natural Science Foundation of Shandong Province (ZR2015FL009 and ZR2014FL020), the Key Research and Development Program of Shandong Province (2016GGX101023), and the Science Foundation of Binzhou University (BZXYG1524).
Author information
Authors and Affiliations
Corresponding author
Additional information
Haijun Wang received his MS degree in School of Information Science and Engineering from Shandong University, China in 2007. He is currently a PhD candidate in the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, China. He is also with the Flying College of Bin Zhou University, China. His research interests include visual tracking and image segmentation.
Hongjuan Ge received her PhD degree in electric machine and electric appliance from Nanjing University of Aeronautics and Astronautics, China in 2007. She is a professor in the College of Civil Aviation in Nanjing University of Aeronautics and Astronautics, China. Her current research interests include electric machine appliance and airplane equipment design research.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Wang, H., Ge, H. Visual tracking using discriminative representation with ℓ2 regularization. Front. Comput. Sci. 13, 199–211 (2019). https://doi.org/10.1007/s11704-017-6434-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-017-6434-9