Fast Object Tracking Based on L2-norm Minimization and Compressed Haar-like Features Matching
WU Zhengping①② YANG Jie① CUI Xiaomeng① ZHANG Qingnian①
①(The Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan 430070, China) ②(College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)
Under the framework of the Bayesian inference, tracking methods based on PCA subspace and L2-norm minimization can deal with some complex appearance changes in the video scene successfully. However, they are prone to drifting or failure when the target object undergoes pose variation or rotation. To deal with this problem, a fast visual tracking method is proposed based on L2-norm minimization and compressed Haar-like features matching. The proposed method not only removes square templates, but also presents a simple but effective observation likelihood, and its robustness to pose variation and rotation is strengthened by Haar-like features matching. Compared with other popular method, the proposed method has stronger robustness to abnormal changes (e.g. heavy occlusion, drastic illumination change, abrupt motion, pose variation and rotation, etc). Furthermore, it runs fast with a speed of about 29 frames/s.
吴正平，杨杰，崔晓梦，张庆年. 融合L2范数最小化和压缩Haar-like特征匹配的快速目标跟踪[J]. 电子与信息学报, 2016, 38(11): 2803-2810.
WU Zhengping, YANG Jie, CUI Xiaomeng, ZHANG Qingnian. Fast Object Tracking Based on L2-norm Minimization and Compressed Haar-like Features Matching. JEIT, 2016, 38(11): 2803-2810.
COMANICIU D, RAMESH V, and MEER P. Real-time tracking of non-rigid objects using mean shift[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, USA, 2000: 142-149.
[2]
KATJA N, ESTHER K M, and LUC V G. An adaptive color-based filter[J]. Image Vision Computing, 2003, 21(1): 99-110.
[3]
ROSS D, LIM J, LIN R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1-3): 125-141. doi: 10.1007/s11263- 007-0075-7.
[4]
MEI Xue and LING Haibin. Robust visual tracking using minimization[C]. Proceedings of IEEE International Conference on Computer Vision, Kyoto, Japan, 2009: 1436-1443.
[5]
MEI Xue, LING Haibin, WU Yi, et al. Minimum error bounded efficient tracker with occlusion detection[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado, USA, 2011: 1257-1264.
[6]
BAO Chenglong, WU Yi, LING Haibin, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, USA, 2012: 1830-1837.
[7]
SHI Qinfeng, ERIKSSON A, VAN DEN HENGEL A, et al. Is face recognition really a compressive sensing problem?[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado, USA, 2011: 553-560.
[8]
XIAO Ziyang, LU Huchuan, and WANG Dong. Object tracking with L2_RLS[C]. Proceedings of 21st International Conference on Pattern Recognition, Tsukuba, Japan, 2012: 681-684.
[9]
XIAO Ziyang, LU Huchuan, and WANG Dong. L2-RLS based object tracking[J]. IEEE Transactions on Circuits Systems for Video Technology, 2014, 24(8): 1301-1309. doi: 10.11834/jig.20140105.
QI Meibin, YANG Xun, YANG Yanfang, et al. Real-time object tracking based on L-norm minimization[J]. Journal of Image and Graphics, 2014, 19(1): 36-44. doi: 10.11834/jig. 20140105.
YUAN Guanglin and XUE Mogen. Robust coding via L-norm regularization for visual tracking[J]. Journal of Electronics & Information Technology, 2014, 36(8): 1838-1843. doi: 10.3724/SP.J.1146.2013.01416.
[12]
WU Zhengping, YANG Jie, LIU Haibo, et al. A real-time object tracking via L2-RLS and compressed Haar-like features matching[J]. Multimedia Tools and Applications, 2016: 1-17. doi: 10.1007/s11042-016-3356-8.
[13]
HONG S and HAN B. Visual tracking by sampling tree-structured graphical models[C]. Proceedings of European Conference on Computer Vision, Zurich, Switzerland, 2014: 1-16. [14] ZHUANG Bohan, LU Huchuan, XIAO Ziyang, et al. Visual tracking via discriminative sparse similarity map[J]. IEEE Transactions on Image Processing, 2014, 23(4): 1872-1881. doi: 10.1109/TIP.2014.2308414.
[15]
ZHANG Kaihua, ZHANG Lei, and YANG Minghsuan. Real-time compressive tracking[C]. Proceedings of European Conference on Computer Vision, Florence, Italy, 2012: 864-877. [16] HENRIQUES J, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596. doi: 10.1109/TPAMI. 2014.2345390.
[17]
LI Hanxin, LI Yi, and FATIH P. Deep track: learning discriminative feature representations by convolutional neural networks for visual tracking[C]. Proceedings of the British Machine Vision Conference, Nottingham, United Kingdom, 2014: 110-119.
[18]
WU Zhengping, YANG Jie, LIU Haibo, et al. Robust compressive tracking under occlusion[C]. Proceedings of International Conference on Consumer Electronics, Berlin, Germany, 2015: 298-302.
[19]
WU Yi, LIM J, and YANG Minghsuan. Online object tracking: a benchmark[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, ORegon, USA, 2013: 2411-2418.
