In order to improve the stability and accuracy of the object tracking under nonlinear conditions, an object tracking algorithm based on Partial Least Squares (PLS) representation and stochastic gradient object optimization tracking is proposed. In this method, object tracking is defined as an optimization task that minimizes the representation error and classification loss. Firstly, it expresses object appearance and background information by PLS theory, learns multiple low dimensional and discriminative subspaces to describe the nonlinear appearance changes of the object. Then, a joint optimization objective function based on deterministic search mechanism is proposed. Furthermore, an stochastic gradient classifier based on incremental features updating is proposed, and make sure that it can achieve a stable tracking. Experiments show favorable performance of the proposed algorithm on sequences where the targets undergo a variety complex changes on foreground and background.
YANG H, SHAO L, ZHENG F, et al. Recent advances and trends in visual tracking: A review[J]. Neurocomputing, 2011, 74(18): 3823-3831.
[2]
ROSS D, LIM J, LIN R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(3): 125-141.
[3]
ZHANG L and VANDER MATTEN L J P. Preserving structure in model-free tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(4): 756-769.
[4]
ORON S, BAR-HILLEL A, LEVI D, et al. Locally orderless tracking[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, 2012: 1940-1947.
[5]
ZHANG S, YAO H, ZHOU H, et al. Robust visual tracking based on online learning sparse representation[J]. Neurocomputing, 2013, 100(1): 31-40.
[6]
POLING B, LEMAN G, and SZLAM A. Better feature tracking through linear subspace constraints[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, 2014: 3454-3461.
[7]
GRABNER H, LEISTNER C, and BISCHOF H. Semi- supervised on-line boosting for robust tracking[C]. European Conference on Computer Vision, Crete Greece, 2010: 234-247.
[8]
BABENKO B, YANG M H, and BELONGIE S. Visual tracking with online multiple instance learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 2011: 983-990.
[9]
KALAL Z, MATAS J, and MIKOLAJCZYK K. P-N learning: Bootstrapping binary classifiers by structural constraints[C]. IEEE Conference on Computer Vision and Pattern Recognition, California, 2010: 49-56.
[10]
WANG Qing, CHEN Feng, XU Wenli, et al. Online discriminative object tracking with local sparse representation[J]. IEEE Workshop on the Applications of Computer Vision, 2012, 12(4): 425-432.
[11]
CHEN Feng, WANG Qing, WANG Song, et al. Object tracking via appearance modeling and sparse representation [J]. Image and Vision Computing, 2013, 29(11): 787-796.
[12]
ROSIPAL R and KRAMER N. Overview and recent advances in partial least squares[J]. Latent Structure and Feature Selection, 2010, 18(3): 34-51.
[13]
HU W, LI W, ZHANG X, et al. Single and multiple object tracking using a multi-feature joint sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(4): 816-833.
[14]
ZOU H and HASTIE T. Regularization and variable selection via the elastic net[J]. Journal of the Royal Statistical Society: Series B, 2011, 67(2): 301-320.
[15]
BORDERS A, BOTTOU L, and GALLINARI P. Sgd-qn: careful quasi-newton stochastic gradient descent[J]. The Journal of Machine Learning Research, 2014, 98(10): 1737-1754.
[16]
WU Y, LIM J, and YANG M H. Object tracking benchmark [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848.
