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http://dx.doi.org/10.3837/tiis.2019.03.020

Multi-feature local sparse representation for infrared pedestrian tracking  

Wang, Xin (College of Computer and Information, Hohai University)
Xu, Lingling (College of Computer and Information, Hohai University)
Ning, Chen (School of Physics and Technology, Nanjing Normal University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.3, 2019 , pp. 1464-1480 More about this Journal
Abstract
Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.
Keywords
Infrared; pedestrian tracking; sparse representation; multiple features;
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1 Xin Wang and Zhenmin Tang, "Modified particle filter-based infrared pedestrian tracking", Infrared Physics & Technology, vol. 53, no. 4, pp. 280-287, July, 2010.   DOI
2 M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon and Tim Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188, February, 2002.   DOI
3 Katja Nummiaro, Esther Koller-Meier and Luc Van Gool, "An adaptive color-based particle filter," Image and Vision Computing, vol. 21, no. 1, pp. 99-110, January, 2003.   DOI
4 Jiangtao Wang, Debao Chen, Haiyan Chen and Jingyu Yang, "On pedestrian detection and tracking in infrared videos", Pattern Recognition Letters, vol. 33, no. 6, pp. 775-785, April, 2012.   DOI
5 Xin Wang, Lei Liu and Zhenmin Tang, "Infrared human tracking with improved Mean Shift algorithm based on multi-cue fusion", Applied Optics, vol. 48, no. 21, pp. 4201-4212, July, 2009.   DOI
6 Dorin Comaniciu, Visvanathan Ramesh and Peter Meer, "Real-time tracking of non-rigid objects using mean shift," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 142-149, June 15, 2000.
7 Guang Han, Xingyue Wang, Jixin Liu, Ning Sun and Cailing Wang, "Robust object tracking based on local region sparse appearance model," Neurocomputing, vol. 184, pp. 145-167, April, 2016.   DOI
8 Bohan Zhuang, Huchuan Lu, Ziyang Xiao and Dong Wang, "Visual tracking via discriminative sparse similarity map," IEEE Transactions on Image Processing, vol. 23, no. 4, pp. 1872-1881, April, 2014.   DOI
9 Xue Mei and Haibin Ling, "Robust visual tracking and vehicle classification via sparse representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no.11, pp. 2259-2272, April, 2011.   DOI
10 Michael Elad and Michal Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Transactions on Image Processing, vol. 15, no. 12, pp. 3736-3745, November, 2006.   DOI
11 Jian Zhang, Debin Zhao and Wen Gao, "Group-based sparse representation for image restoration," IEEE Transactions on Image Processing, vol. 23, no. 8, pp. 3336-3351, May, 2014.   DOI
12 Xin Wang, Siqiu Shen, Chen Ning, Mengxi Xu and Xijun Yan, "A sparse representation-based method for infrared dim target detection under sea-sky background," Infrared Physics & Technologu, vol. 71, pp. 347-355, July, 2015.   DOI
13 Tanaya Guha and Rabab K Ward, "Learning sparse representations for human action recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 8, pp. 1576-1588, August, 2012.   DOI
14 Xin Wang, Siqiu Shen, Chen Ning, Fengchen Huang and Hongmin Gao, "Multi-class remote sensing object recognition based on discriminative sparse representation," Applied Optics, vol. 55, no. 6, pp. 1381-1394, 2016.   DOI
15 Julien Mairal, Francis Bach and Jean Ponce, "Task-driven dictionary learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(4): 791-804, 2012.   DOI
16 Xianguo Yu, Qifeng Yu, Yang Shang and Hongliang Zhang, "Dense structural learning for infrared object tracking at 200+ Frames per Second," Pattern Recognition Letters, vol. 100, pp. 152-159, December, 2017.   DOI
17 Fabrizio Lamberti, Andrea Sanna and Gianluca Paravati, "Improving robustness of infrared target tracking algorithms based on template matching", IEEE Transactions on Aerospace and Electronic Systems, vol. 47, no. 2, pp. 1467-1480, April, 2011.   DOI
18 Suk Jin Lee, Gaurav Shah, Arka Aloke Bhattacharya and Yuichi Motai, "Human tracking with an infrared camera using a curve matching framework", EURASIP Journal on Advances in Signal Processing, vol. 2012, pp. 99, May, 2012.   DOI
19 Xin Wang, Chen Ning and Lizhong Xu, "Spatiotemporal Difference-of-Gaussians filters for robust infrared small target tracking in various complex scenes," Applied Optics, vol. 54, no. 7, pp. 1573-1586, July, 2015.   DOI
20 C. S. Asha and A. V. Narasimhadhan, "Robust infrared target tracking using discriminative and generative approaches," Infrared Physics & Technology, vol. 85, pp. 114-127, June, 2017.   DOI
21 Xue Mei and Haibin Ling, "Robust visual tracking using l1 minimization," in Proc. Of IEEE Conference on Computer Vision, pp. 1436-1443, 2009.
22 Xu Jia, Huchuan Lu and MingHsuan Yang, "Visual tracking via adaptive structural local sparse appearance model," in Proc. Of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1822-1829, June 16-21, 2012.
23 Baiyang Liu, Junzhou Huang, Casimir Kulikowski and Lin Yang, "Robust visual tracking using local sparse appearance model and K-selection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 12, pp. 2968-2981, December, 2013.   DOI
24 John Wright, Yi Ma, Julien Mairal, Guillermo Sapiro, Thomas S. Huang and Shuicheng Yan, "Sparse Representation for Computer Vision and Pattern Recognition," Proceedings of the IEEE, vol. 98, no. 6, pp. 1031-1044, June, 2010.   DOI
25 Changjiang Yang, R. Duraiswami and L. Davis, "Efficient mean-shift tracking via a new similarity measure," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 176-183, June 20-25, 2005.
26 Michal Aharon, Michael Elad and Alfred Bruckstein, "K-svd: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311-4322, November, 2006.   DOI
27 StCphane G. Mallat and Zhifeng Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3397-3415, 1993.   DOI
28 Joel A. Tropp and Anna C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655-4666, December, 2007.   DOI
29 Paul Brasnett, Lyudmila Mihaylova, David Bull and Nishan Canagarajah, "Sequential Monte Carlo tracking by fusing multiple cues in video sequences," Image and Vision Computing, vol. 25, no. 8, pp. 1217-1227, August, 2007.   DOI
30 David A. Ross, Jongwoo Lim, RueiSung Lin and MingHsuan Yang, "Incremental learning for robust visual tracking," International Journal of Computer Vision, vol. 77, no. 1-3, pp. 125-141, May, 2008.   DOI
31 J. Davis and M. Keck, "A two-stage approach to person detection in thermal imagery, " in Proc.of Workshop on Applications of Computer Vision, January, pp. 364-369, 2005.
32 Dilip K. Prasad and Michael S. Brown, "Online tracking of deformable objects under occlusion using dominant points," Journal of the Optical Society of America A, vol. 30, no. 8, pp. 1484-1491, 2013.   DOI
33 Xin Wang, Siqiu Shen, Chen Ning, Yuzhen Zhang and Guofang Lv, "Robust object tracking via local discriminative sparse representation," Journal of the Optical Society of America A, vol. 34, no. 4, pp. 533-544, 2017.   DOI
34 Masahiro Yasuno, Noboru Yasuda and Masayoshi Aoki, "Pedestrian Detection and Tracking in Far Infrared Images," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition Workshop, Washington, pp. 125, June 27-31, 2004.