A Robust Kernelized Correlation Tracking Algorithm for Infrared Targets Based on Ensemble Learning
XIE Tao① WU Ensi②
①(College of Computer and Information Science, Southwest University, Chongqing 400715, China) ②(Office of Educational Administration, Chongqing Normal University, Chongqing 401331, China)
Abstract:In the infrared object tracking, the single classifier is not enough to fit the multimodal data due to the complex background information of the target and the significant change in the appearance. In this paper, Kernelized Correlation Filters (KCF) tracking algorithm is used to integrate kernelized correlation classifiers into one framework through ensemble learning. It uses the KCF classifier that has analytical solutions to balance the contradiction between the robustness and instantaneity, thereby addressing the complex background and significant appearance changes, and consequently significantly improving the tracking performance and stability. To verify the effectiveness of the algorithm, this paper uses two kernelized correlation trackers to learn a strong classifier. The qualitative and quantitative experiments show that the proposed algorithm outperforms the traditional KCF algorithm, and the tracking speed is superior to most of the comparison algorithms.
LI Shaoyi, LIANG Shuang, ZHANG Kai, et al. Research of infrared compressive imaging based point target tracking method[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1639-1645. doi: 10.11999/JEIT 141324.
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.
LUO Huilan, ZHONG Baokang, and KONG Fansheng. Tracking using weighted block compressed sensing and location prediction[J]. Journal of Electronics & Information Technology, 2015, 37(5): 1160-1166. doi: 10.11999/JEIT 140997.
[4]
ROSS D A, LIM J, LIN R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1): 125-141. doi: 10.1007/s11263-007-0075 -7.
[5]
ZHONG W, LU H, and YANG M H. Robust object tracking via sparse collaborative appearance model[J]. IEEE Transactions on Image Processing, 2014, 23(5): 2356-2368. doi: 10.1109/TIP.2014.2313227.
XUE Yizhe and WANG Tuo. Object tracking based on cost-sensitive Adaboost algorithm[J]. Chinese Journal of Image and Graphics, 2016, 21(5): 544-555.
[7]
ZHANG K and SONG H. Real-time visual tracking via online weighted multiple instance learning[J]. Pattern Recognition, 2013, 46(1): 397-411. doi: 10.1016/j.patcog.2012.07.013.
[8]
BABENKO B, YANG M H, and BELONGIE 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.
[9]
HU J, LU J, and TAN Y P. Deep metric learning for visual tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 26(11): 2056-2068. doi: 10.1109/ TCSVT.2015.2477936.
[10]
KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. Imagenet classification with deep convolutional neural networks[C]. Proceedings of the Advances in Neural Information Processing Systems, Nevada, USA, 2012: 1097-1105. doi: 10.1145/3065386.
[11]
TANG Z, WANG S, HUO J, et al. Bayesian framework with non-local and low-rank constraint for image reconstruction [C]. Proceedings of the Journal of Physics, 2017, 787: 012008. doi: 10.1088/1742-6596/787/1/012008.
[12]
HENRIQUES J F, 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.
[13]
LIU T, WANG G, and YANG Q. Real-time part-based visual tracking via adaptive correlation filters[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 4902-4912. doi: 10.1109/ CVPR.2015.7299124.
[14]
MATTHEWS L, ISHIKAWA T, and BAKER S. The template update problem[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(6): 810-815. doi: 10.1109/TPAMI.2004.16.
HOU Zhiqiang, HUANG Anqi, YU Wangsheng, et al. Visual object tracking method based on local patch model and model update[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1357-1364. doi: 10.11999/JEIT 141134.
SHU Qiaoping, LIU Yuan, BU Yingqiao, et al. Multi-example learning target tracking algorithm based on sparse expression [J]. Computer Engineering, 2013, 39(3): 213-217.
[18]
VIOLA P, JONES M J, and SNOW D. Detecting pedestrians using patterns of motion and appearance[J]. International Journal of Computer Vision, 2005, 63(2): 153-161. doi: 10.1007/s11263-005-6644-8.
[19]
KALAL Z, MIKOLAJCZYK K, and MATAS J. Tracking- learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422. doi: 10.1109/TPAMI.2011.239
[20]
ZHANG K, ZHANG L, LIU Q, et al. Fast visual tracking via dense spatio-temporal context learning[C]. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland, 2014: 127-141. doi: 10.1007/978-3-319-10602- 1_9.