Wang Jiangtao, Chen Debao, Li Suwen, Yang Yijun, Yang Jingyu. IR object tracking method via online adaptive subspace selection[J]. Infrared and Laser Engineering, 2013, 42(9): 2579-2583.
| Citation:
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Wang Jiangtao, Chen Debao, Li Suwen, Yang Yijun, Yang Jingyu. IR object tracking method via online adaptive subspace selection[J]. Infrared and Laser Engineering, 2013, 42(9): 2579-2583.
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IR object tracking method via online adaptive subspace selection
- 1.
School of Physical and Electronic Information,Huaibei Normal University,Huaibei 235000,China;
- 2.
College of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094,China
- Received Date: 2013-01-15
- Rev Recd Date:
2013-02-16
- Publish Date:
2013-09-25
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Abstract
The subspace constructing strategy of classic subspace-based tracking schemes is to select appropriate subspaces with maximum energy, in this strategy the discriminability between the target and background is neglected, so when the target and background have similar appearance the tracking system's performance may be degenerated. To solve the problems of IR image's low SNR and low contrast, a novel subspace selecting method was proposed based on analyzing the discriminability between the target and background. The IR object tracking process was realized by the particle filter with the provided subspace selecting strategy. In this case, based on the prior knowledge of the particles distributions and the target state, different subspace's tracking ability by considering both the feature difference and the particles' approximation level to the target was estimated firstly, then the optimal subspaces were selected to realized the IR target tracking. Experiments on several complex scenes indicate that the proposed algorithm has better performance than the classic one.
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References
|
[1]
|
|
|
[2]
|
Bay H, Ess A, Tuytelaars T, et al. SURF: speeded up robust features[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359. |
|
[3]
|
|
|
[4]
|
Wang S, Lu H C, Yang F, et al. Superpixel tracking[C]//In Proceedings of 2011 International Conference on Computer Vision, 2011: 1323-1330. |
|
[5]
|
Hu J, Juan C, Wang J. A spatial-color mean-shift object tracking algorithm with scale and orientation estimation[J]. Pattern Recognition Letters, 2008, 29(16): 2165-2173. |
|
[6]
|
|
|
[7]
|
Erdem E, Dubuisson S, Bloch I. Visual tracking by fusing multiple cues with context-sensitive reliabilities[J]. Pattern Recognition, 2012, 45(5): 1948-1959. |
|
[8]
|
|
|
[9]
|
Yang H X, Shao L, Zheng F, et al. Recent advances and trends in visual tracking: A review[J]. Neurocomputing, 2011, 74 (18): 3823-3831. |
|
[10]
|
|
|
[11]
|
|
|
[12]
|
Ross D A, Lim J, Lin R, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1-3): 125-141. |
|
[13]
|
Kong Jun, Tang Xinyi, Jiang Min, et al. IR target tracking based on scale space feature points matching[J]. Infrared and Laser Engineering, 2011, 40(11): 2104-2109. (in Chinese) |
|
[14]
|
|
|
[15]
|
|
|
[16]
|
Wang Jiangtao, Yang Jingyu. Shape-based human detection in infrared image sequences[J]. Journal of Infrared and Millimeter Waves, 2007, 26(6): 437-442. (in Chinese) |
|
[17]
|
Deng He, Liu Jianguo, Chen Zhong. Infrared small target detection based on modified local entropy and EMD[J]. Chinese Optics Letters, 2010, 8(1): 24-28. |
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