In order to improve the stability and accuracy of the object tracking under different conditions, an online object tracking algorithm based on Gray-Level Co-occurrence Matrix (GLCM) and third-order tensor is proposed. First, the algorithm extracts the gray-level information of target area to describe the two high discrimination features of target by GLCM, the dynamic information about target changing is fused by third-order tensor theory, and the third-order tensor appearance model of the object is constructed. Then, it uses bilinear space theory to expand the appearance model, and implements the incremental learning. Updating of model by online model’s characteristic value description, thus computation of the model updating is greatly reduced. Meanwhile, the static observation model and adaptive observation model are constructed, and secondary combined stable tracking of object is achieved by dynamic matching of two observation models. Experimental results indicate that the proposed algorithm can effectively deal with the moving object tracking on a variety of challenging scenes, and the average tracking error is less than 9 pixels.
金广智,石林锁,崔智高,刘浩,牟伟杰. 结合GLCM与三阶张量建模的在线目标跟踪[J]. 电子与信息学报, 2016, 38(7): 1609-1615.
JIN Guangzhi, SHI Linsuo,CUI Zhigao, LIU Hao, MU Weijie. Online Object Tracking Based on Gray-level Co-occurrence Matrix and Third-order Tensor. JEIT, 2016, 38(7): 1609-1615.
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]
HO J, LEE K C, YANG M H, et al. Visual tracking using learned linear subspaces[C]. IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, USA, 2012: 782-789.
[3]
LEE K C and KRIEGMAN D. Online learning of probabilistic appearance manifolds for video-based recognition and tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 852-859.
[4]
ROSS D, LIM J, LIN R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2013, 77(3): 125-141.
[5]
LATHAUWER L, MOOR B, and VANDEWALLE J. On the best rank-1 and approximation fhigherorder tensors[J]. SIAM Journal of Matrix Analysis and Applications, 2000, 21(4): 1324-1342.
[6]
LI X, HU W, ZHANG Z, et al. Visual tracking via incremental Log-Euclidean Riemannian sub-space learning [C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 578-585.
[7]
LI X, HU W, ZHANG Z, et al. Robust visual tracking based on incremental tensor subspace learning[C]. IEEE International Conference on Computer Vision, Barcelona, Spain, 2012: 1008-1016.
ZHONG Ye, YANG Xiaoming, and JIAO Licheng. Texture classification based on multiresolution co-occurrence matrix [J]. Journal of Computer Research and Development, 2014, 48(11): 1991-1999.
BO Hua, MA Fulong, and JIAO Licheng. Research on computation of GLCM of image texture[J]. Acta Electronica Sinica, 2006, 34(1): 155-158.
[10]
CLAUSI D A and DENG H. Design-based texture Feature using Gabor filters and co-occurrence probabilities[J]. IEEE Transactions on Image Processing, 2005, 14(7): 925-936.
WU Guangwen, ZHANG Aijun, and WANG Changming. Novel optimization method for projection matrix in compress sensing theory[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1681-1687. doi: 10.11999/JEIT 141450.
[12]
KHAN Z H and GUI Y H. Online domain-shift learning and object tracking based on nonlinear dynamic models and particle filters on Riemannian manifolds[J]. Computer Vision and Image Understanding, 2014, 15(6): 97-114.
QI Yuanchen, WU Chengdong, CHEN Dongyue, et al. Superpixel tracking based on sparse representation[J]. Journal of Electronics & Information Technology, 2015, 37(3): 529-535. doi: 10.11999/JEIT140374.
[14]
CHENG X, LI N, ZHOU T, et al. Object tracking via collaborative multi-task learning and appearance model updating[J]. Applied Soft Computing, 2015, 31: 81-90.
[15]
ZHONG W, LU H, and YANG M H. Robust object tracking via sparse collaborative appearance model[J]. IEEE Transactions on Image Processing, 2014, 23(5): 2356-2368.
[16]
WU Y, LIM J, and YANG M H. Online object tracking: A benchmark[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2411-2418.