Abstract：The local similarity measurements are usually used for improving the tracking robustness under the complex scene. However, this method have drawbacks in cases of partial occlusion, deformation and rotation. For example, the method only considers traditional similarity measurements of targets and templates, results in the matching errors to lead to tracking failure. In this paper, a target tracking algorithm is proposed based on measurements of the local difference similarities. The presented method has the following advantages: firstly, both similarities and differences are considered for measurement; secondly, the differential weight learning of the local region is carried out to improve the accuracy of sub-block difference measurement; at last, an effective and efficient tracker is designed based on the difference analysis and a simple update manner within the particle filter framework. Experimental results show that the proposed algorithm achieves better performance than traditional competing methods in various factors, such as illumination changes, part occlusion, scale changes and so on.
LIU Hongliang, ZHOU Shenghua, LIU Hongwei, et al. An integrated target detection and tracking algorithm with constant track false alarm rate[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1072-1078. doi: 10.11999/JEIT150638.
[2]
MARKUS Z, THOMAS N, and ANDREAS K. Tracking human locomotion by relative positional feet tracking[C]. 2015 IEEE Virtual Reality (VR), Arles, France, 2015: 317-318.
[3]
EVANGELO S, HATICE G, and ANDREA C. Automatic analysis of facial affect: A survey of registration, representation, and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(6): 1113-1133.
[4]
SAI Y, BO X, Li P Y, et al. Robust scene matching method based on sparse representation and iterative correction[J]. Image and Vision Computing, 2017, 60(4): 115-123.
[5]
PENG P L, ERIK B, and HAI B L. Encoding color information for visual tracking: algorithms and benchmark[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5630-5644. doi: 10.1109/TIP.2015.2482905.
[6]
BABENKO B, YANG M H, and BELONG S. Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632. doi: 10.1109/TPAMI.2010.226.
[7]
ZHANG K, ZHANG L, and YANG M H. Real-time object tracking via online discriminative feature selection[J]. IEEE Transactions on Image Processing, 2013, 22(12): 4664-4677. doi: 10.1109/TIP.2013.2277800.
[8]
LAURA S L and ERIK L M. Distribution fields for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 1910-1917.
[9]
BAO C, WU Y, LING H, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]. IEEE Conference Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, 2012: 1830-1837.
[10]
MEI X, LING H, and WU Y. Efficient minimum error bounded particle resampling LI tracker with occlusion detection[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2661-2675.
[11]
KWON J and LEE K M. Highly non-rigid object tracking via patch-based dynamic appearance modeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2427-2441.
[12]
LIU B, HUANG J, YANG L, et al. Robust tracking using local sparse appearance model and k-selection[C]. IEEE Conference on Computer Vision and Pattern Recognition. Colorado, CO, USA, 2011, 201: 1313-1320.
[13]
JIA X, LU H, and YANG M H. Visual tracking via adaptive structural local sparse appearance model[C]. IEEE Conference Computer Vision and Pattern Recognition (CVPR), USA, 2012: 1822-1829.
[14]
ADAM A, RIVLINi E, and SHIMSHONI I. Robust fragment- based tracking using the integral histogram[C]. IEEE Conference Computer Vision and Pattern Recognition (CVPR), USA, 2006: 798-805.
[15]
HE S, YANG Q, and Yang M H. Visual tracking via locality sensitive histograms[C]. IEEE Conference Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA, 2013: 2427-2434.
[16]
JIA Y M, HUA B, JI Z, et al. Robust feature matching for remote sensing image registration via locally linear transforming[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12): 6469-6481. doi: 10.1109/ TGRS.2015.2441954.
[17]
WANG D, LU H C, and BO C J. Visual tracking via weighted local cosine similarity[J]. IEEE Transactions on Cybernetics, 2015, 45(9): 1838-1850. doi: 10.1109/TCYB. 2014.2360924.
[18]
LIU H C, LI S T, and FANG L Y. Robust object tracking based on principal component analysis and local parse representation[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(11): 2863-2875. doi: 10.1109/TIM. 2015.2437636.
[19]
ZHUANG B H, LU H C, XIAN Z Y, 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.
BI Duyan, KU Tao, ZHA Yufei, et al. Scale-adaptive object tracking based on color names histogram[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1009-1106. doi: 10.11999/JEIT150921.
ZHAN Ronghui, LIU Shengqi, OU Jianping , et al. Improved multi target track before detect algorithm using the sequential monte carlo probability hypothesis density filter[J]. Journal of Electronics & Information Technology, 2014, 36(11): 2593-2599. doi: 10.3724/SP.J.1146.2013.02029.
[22]
ORONR S and AHARON B H. Locally orderless tracking[J]. International Journal of Computer Vision, 2015, 111(2): 213-228. doi: 10.1109/CVPR.2012.6247895.
