Anti-occluded infrared target tracking with salient feature space

Ma Tianyi; Zhang Huixiang; Song Minmin; Niu Saisai

doi:10.3788/IRLA201746.0304002

Aiming at the lack of target texture information in infrared target tracking, strong coupling between the target and the background, and the case of occlusion causing that the characteristic information cannot be extended, the anti-occlusion algorithm for infrared target tracking with salient feature space was proposed. Firstly, by analyzing the characteristics of the infrared target, the salient feature space was generated by using multi-scale saliency, contrast and information entropy, and the regions were clustered with the super-pixel feature distance and spatial distance to highlight the infrared target area. By quantifying the different regions of the infrared image, the saliency map was generated. Based on the saliency map and the original image, a plurality of target candidate regions was generated as the tracking algorithms input. Finally, the spatial distribution field matrices of the infrared target were matched by way of the global candidate regions. At the same time, the occlusion detection mechanism on account of the change of the salience regions and the inter-frame variation curve of feature similarity was established. Experimental results on different IR test sets show that the proposed infrared target algorithm can achieve better tracking performance under the occlusion condition, which effectively enhances the robustness of the tracking algorithm.

Anti-occluded infrared target tracking with salient feature space

1. Shanghai Institute of Spaceflight Control Technology,Shanghai 201109,China

Received Date: 2016-07-10

Rev Recd Date: 2016-08-20

Publish Date: 2017-03-25

Key words:

Abstract: Aiming at the lack of target texture information in infrared target tracking, strong coupling between the target and the background, and the case of occlusion causing that the characteristic information cannot be extended, the anti-occlusion algorithm for infrared target tracking with salient feature space was proposed. Firstly, by analyzing the characteristics of the infrared target, the salient feature space was generated by using multi-scale saliency, contrast and information entropy, and the regions were clustered with the super-pixel feature distance and spatial distance to highlight the infrared target area. By quantifying the different regions of the infrared image, the saliency map was generated. Based on the saliency map and the original image, a plurality of target candidate regions was generated as the tracking algorithms input. Finally, the spatial distribution field matrices of the infrared target were matched by way of the global candidate regions. At the same time, the occlusion detection mechanism on account of the change of the salience regions and the inter-frame variation curve of feature similarity was established. Experimental results on different IR test sets show that the proposed infrared target algorithm can achieve better tracking performance under the occlusion condition, which effectively enhances the robustness of the tracking algorithm.

HTML

Reference (12)

[1]	Qiao Liyong, Xu Lixin, Gao Min. Infrared target tracking using bandwidth adaptive mean shift[J]. Infrared and Laser Engineering, 2015, 44(1):354-362. (in Chinese)乔立永, 徐立新, 高敏. 带宽自适应均值偏移红外目标跟踪[J]. 红外与激光工程, 2015, 44(1):354-362.
[2]	Liu Hongbin, Chang Faliang. Moving object detection by optical flow method based on adaptive weight coefficient[J]. Optics and Precision Engineering, 2016, 24(2):460-468. (in Chinese)刘洪彬, 常发亮. 权重系数自适应光流法运动目标检测[J]. 光学精密工程, 2016, 24(2):460-468.
[3]	Yang Mingdong, Wang Jianyu, Jia Jianjun, et al. Research on technologies of space area targets high-precision tracking based on SWAD algorithm[J]. Infrared and Laser Engineering, 2016, 45(2):228002. (in Chinese)杨明冬, 王建宇, 贾建军, 等. 基于SWAD算法的空间面目标高精度跟踪技术研究[J]. 红外与激光工程, 2016, 45(2):228002.
[4]	Yang Dedong, Cai Yuzhu, Mao Ning, et al. Long-term object tracking based on kernelized correlation filters[J]. Optics and Precision Engineering, 2016, 24(8):2037-2049. (in Chinese)杨德东, 蔡玉柱, 毛宁, 等. 采用核相关滤波器的长期目标跟踪[J]. 光学精密工程, 2016, 24(8):2037-2049.
[5]	Gao Wen, Zhu Ming, He Baigen, et al. Overview of target tracking technology[J]. Chinese Optics, 2014, 7(3):365-375. (in Chinese)高文, 朱明, 贺柏根, 等. 目标跟踪技术综述[J]. 中国光学, 2014, 7(3):365-375.
[6]	Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7):1409-1422.
[7]	Hare S, Golodetz S, Saffari A, et al. Struck:structured output tracking with kernels[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10):2096-2109.
[8]	Cheng Peirui, Wang Jianli, Wang Bin, et al. Salient object detection based on multi-scale region contrast[J]. Chinese Optics, 2016, 9(1):97-105. (in Chinese)成培瑞, 王建立, 王斌, 等. 基于多尺度区域对比的显著目标识别[J]. 中国光学, 2016, 9(1):97-105.
[9]	Alexe B, Deselaers T, Ferrari V. Measuring the objectness of image windows[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11):2189-2202.
[10]	Rodriguez A, Laio A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191):1492-1496.
[11]	Sevilla-Lara L, Learned-Miller E. Distribution fields for tracking[C]//Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference, 2012:1910-1917.
[12]	Wu Y, Lim J, Yang M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1834-1848.

Anti-occluded infrared target tracking with salient feature space

doi: 10.3788/IRLA201746.0304002

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Related

Proportional views