Fast Robust Visual Tracking Based on Coding Transfer
XUE Mogen① LIU Wenzhuo① YUAN Guanglin② QIN Xiaoyan②
①(Anhui Province Key Laboratory of Polarization Imaging Detection Technology, Army Officer Academy of PLA, Hefei 230031, China) ②(Eleventh Department, Army Officer Academy of PLA, Hefei 230031, China)
The sparsity constraint of the L1 tracker’s representation model makes it have good robustness towards partial occlusion. However, the tracking speed of the L1 tracker is slow. To solve this study, this paper proposes a coding transfer method for visual tracking. By making use of the low-resolution dictionary to calculate coefficients of the candidate targets and the high-resolution dictionary to construct the observation likelihood model, the method reduces calculation amount effectively in the process of tracking. In order to improve the precision of coding transfer and the ability of the dictionary to overcome the background clutters, this study proposes an online robust discrimination joint dictionary learning model to update the dictionaries. The experimental results demonstrate that the proposed method has good robustness and superior tracking speed.
WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227. doi: 10.1109/TPAMI.2008.79.
[2]
MEI X and LING H B. Robust visual tracking using L1 minimization[C]. IEEE International Conference on Computer Vision, Kyoto, Japan, 2009: 1436-1443. doi: 10.1109/ICCV.2009.5459292.
[3]
WANG D, LU H C, and YANG M H. Least soft-thresold squares tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013: 2371-2378. doi: 10.1109/CVPR.2013.307.
[4]
ZHANG X Q, LI W, HU W M, et al. Block covariance based L1 tracker with a subtle template dictionary[J]. Pattern Recognition, 2013, 46(7): 1750-1761. doi: 10.1016/j.patcog. 2012.08.015.
[5]
WANG L J, OUYANG W L, WANG X G, et al. Visual tracking with fully convolutional networks[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3119-3127. doi: 10.1109/ICCV.2015. 357.
LIU B Y, LIN Y, HUANG J Z, et al. Robust and fast collaborative tracking with two stage sparse optimization[C]. Europe Conference on Computer Vision, Crete, Greece, 2010: 624-637. doi: 10.1007/978-3-642-15561-1_45.
[9]
MEI X, LING H B, WU Y, et al. Minimum error bounded efficient L1 tracker with occlusion detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, 2011: 1257-1264. doi: 10.1109/ CVPR. 2011.5995421.
[10]
BAO C L, WU Y, LING H B, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, America, 2012: 1830-1837. doi: 10.1109/CVPR.2012.6247881.
[11]
ZHANG T Z, GHANEM B, LIU S, et al. Robust visual tracking via multi-task sparse learning[J]. International Journal of Computer Vision, 2013, 101(2): 367-383. doi: 10.1109/CVPR.2012.6247908.
YUAN Guanglin and XUE Mogen. Visual tracking based on sparse dense structure representation and online robust dictionary learning[J]. Journal of Electronics & Information Technology, 2015, 37(3): 536-542. doi: 10.11999/JEIT140507.
YUAN Guanglin and XUE Mogen. Sparsity-constrained and dynamic group structured sparse coding for robust visual tracking[J]. Acta Electronica Sinica, 2015, 43(8): 1499-1505. doi: 10.3969/j.issn.0372-2112.2015.08.005.
[14]
YANG J C, WRIGHT J, HUANG T, et al. Image super- resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873. doi: 10.1109/TIP. 2010.2050625.
[15]
WU Y, LING H B, YU J Y, et al. Blurred target tracking by blur-driven tracker[C]. International Conference on Computer Vision, Barcelona, Spain, 2011: 1100-1107. doi: 10.1109/ICCV.2011.6126357.
[16]
WU Y, LIM J, and YANG M H. Online object tracking: A benchmark[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013: 2411-2418. doi: 10.1109/CVPR.2013.312.