Object Tracking via Multi-task Least-Soft-Threshold Squares Regression
YASIN Musa1,2, ZHAO Chun-Xia1
1.School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094 2.School of Mechanical Engineering, Xinjiang University, Urumqi 830046
Abstract In visual object tracking, model representation is one of the core issues directly affecting tracking efficiency. The object model representation needs an efficient template learning from the online complicated data to adapt to the variations caused by intrinsic or extrinsic factors during the tracking process. In this paper, the detailed descriptions of robust object model representation algorithm is provided, and a novel multi-task least-soft-threshold squares tracking framework (MLST) is proposed. In the proposed scheme, the observation model is treated as a multi-task linear regression problem,and the appearance models of the candidate target under different states are represented by using object templates and additive independently and identically distributed Gaussian-Laplacian noise assumption, so that the tracker is well adopted to some complex scenes and is able to predict the accurate state of the object in every frame. Extensive experiments are carried out to validate that the online learning scheme improves the representation accuracy by exploring some specific properties of the target in every task, and the tracker maintains the best ability to achieve favorable performance. The experimental results show that the proposed algorithm performs favorably against several state-of-the-art tracking algorithms.
[1] Jepson A D, Fleet D J, El-Maraghi T F. Robust Online Appearance Models for Visual Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(10): 1296-1311 [2] Ross D A, Lim J, Lin R S, et al. Incremental Learning for Robust Visual Tracking. International Journal of Computer Vision, 2008, 77(1/2/3): 125-141 [3] Li H X, Shen C H, Shi Q F. Real-Time Visual Tracking Using Compressive Sensing // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 1305-1312 [4] Mei X, Ling H B. Robust Visual Tracking and Vehicle Classification via Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 33(11): 2259-2272 [5] Avidan S. Support Vector Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(8): 1064-1072 [6] Wang Q, Chen F, Xu W L, et al. Online Discriminative Object Tracking with Local Sparse Representation//Proc of the IEEE Workshop on Applications of Computer Vision. Breckenridge, USA, 2012: 425-432 [7] Xie Y, Zhang W S, Li C H. et al. Discriminative Object Tracking via Sparse Representation and Online Dictionary Learning. IEEE Trans on Cybernetics, 2014, 44(4): 539-553 [8] Babenko B, Yang M H, Belongie S. Robust Object Tracking with Online Multiple Instance Learning. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632 [9] Yilmaz A, Javed O, Shah M. Object Tracking: A Survey. ACM Journal of Computing Surveys, 2006, 38(4): 1-45 [10] Wang D, Lu H C, Yang M H. Least Soft-Threshold Squares Tracking // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 2371-2378 [11] Caruana R. Multitask Learning. Machine Learning, 1997, 28(1): 41-75 [12] Zhang T Z, Ghanem B, Liu S, et al. Robust Visual Tracking via Multi-Task Sparse Learning // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012: 2042-2049 [13] Boyd S, Parikh N, Chu E, et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Foundations and Trends in Machine Learning, 2011, 3(1): 1-122 [14] Liu G C, Lin Z C, Yan S C, et al. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2013, 35(1): 171-184 [15] Lin Z C, Chen M M, Ma Y. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. [EB/OL]. [2013-10-18]. http://arxiv.org/pdf/1009.5055v3.pdf [16] Babenko B, Yang M H, Belongie S. Visual Tracking with Online Multiple Instance Learning // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 983-990 [17] Kwon J, Lee K M. Visual Tracking Decomposition // Proc of the 23rd IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 1269-1276 [18] Zhong W, Lu H C, Yang M H. Robust Object Tracking via Sparsity-Based Collaborative Model // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012: 1838-1845 [19] Jia X, Lu H C, Yang M H. Visual Tracking via Adaptive Structural Local Sparse Appearance Model // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012: 1822-1829 [20] Wang D, Lu H C, Yang M H. Online Object Tracking with Sparse Prototypes. IEEE Trans on Image Processing, 2013, 22(1): 314-325
2015, Vol. 28 
