Indirect location method for building target based on automatic selection of auxiliary target

TU Zhijian; ZHANG Tianxu; SANG Hongshi

doi:10.5768/JAO201940.0402004

Journal of Applied Optics > 2019 > 40(4): 603-611. > DOI: 10.5768/JAO201940.0402004

TU Zhijian, ZHANG Tianxu, SANG Hongshi. Indirect location method for building target based on automatic selection of auxiliary target[J]. Journal of Applied Optics, 2019, 40(4): 603-611. DOI: 10.5768/JAO201940.0402004

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Indirect location method for building target based on automatic selection of auxiliary target

1.
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
2.
School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China

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Received Date: January 15, 2019
Revised Date: February 25, 2019

Graphical Abstract

Abstract

Abstract

Aiming at the false alarm or missing problems due to the insignificant and obscured targets in sequence images, an indirect localization method for building targets based on automatic selection of auxiliary targets was proposed to improve the stability of target tracking. In this method, firstly, the region with stable shape and significant gray level in the infrared image was automatically selected as the auxiliary target. After locating the building target, its relative positional relationship with the target was extracted, and then, the relative position was used to indirectly locate the target. Finally, the target was located by fusing the result of direct target recognition and the result of indirect target location, which could update the auxiliary target timely to ensure that it is always in the field of view. The experimental result shows that the false alarm or missing problems can be solved by indirect location, and it can improve the accuracy and robustness of the algorithm during the process of target location.
- infrared,
- building target,
- automatic selection,
- salient region,
- auxiliary target,
- indirect location

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References (15)

References

[1]	YANG X Y, ZHANG T X, LU Y. Building recognition based on geometric model in FLIR image sequences[J]. Journal of Infrared, Millimeter, and Terahertz Waves, 2009, 30(5): 468-483. doi: 10.1007/s10762-009-9470-1
[2]	颜廷法.基于图像序列的特定建筑物识别算法[J].泰山学院学报, 2018, 40(3): 62-66. doi: 10.3969/j.issn.1672-2590.2018.03.009 YAN Ting fa. An algorithm of special building recognition base on image sequence[J]. Journal of Taishan University, 2018, 40(3): 62-66. doi: 10.3969/j.issn.1672-2590.2018.03.009
[3]	苏娟, 徐青松, 刘刚.一种基于边缘匹配的前视红外目标识别算法[J].兵工学报, 2012, 33(3): 271-277. http://d.old.wanfangdata.com.cn/Periodical/bgxb201203004 SU Juan, XU Qingsong, LIU Gang. A forward looking infrared target recognition algorithm based on edge matching[J]. Acta Armamentarii, 2012, 33(3): 271-277. http://d.old.wanfangdata.com.cn/Periodical/bgxb201203004
[4]	明德烈, 田金文.红外前视对一类特殊建筑目标识别技术研究[J].宇航学报, 2010, 31(4): 1190-1194. doi: 10.3873/j.issn.1000-1328.2010.04.040 MING Delie, TIAN Jinwen. Automatic infrared condensing tower target recognition using gradient vector features[J]. Journal of Astronautics, 2010, 31(4): 1190-1194. doi: 10.3873/j.issn.1000-1328.2010.04.040
[5]	高晶, 孙继银, 吴昆, 等.基于形状模板匹配的前视红外目标检测方法[J].北京工业大学学报, 2012, 38(9):1359-1365. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bjgydxxb201209016 GAO Jing, SUN Jiyin, WU Ken, et al. FLIR target detection algorithm based on shape template matching[J]. Journal of Beijing University of Technology, 2012, 38(9): 1359-1365. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bjgydxxb201209016
[6]	WANG D W, ZHANG T X. Building recognition based on indirect location of planar landmark in FLIR image sequences[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2011, 25(3): 431-448. doi: 10.1142/S0218001411008695
[7]	孙大为, 王仕成, 杨东方, 等.基于仿射不变性映射的不显著目标间接定位[J].中国惯性技术学报, 2017, 25(6):782-788. http://d.old.wanfangdata.com.cn/Periodical/zggxjsxb201706015 SUN Dawei, WANG Shicheng, YANG Dongfang, et al. Indirect location algorithm for insignificant targets based on affine invariability mapping[J]. Journal of Chinese Inertial Technology, 2017, 25(6):782-788. http://d.old.wanfangdata.com.cn/Periodical/zggxjsxb201706015
[8]	李超, 吴玉敬, 徐飞飞, 等.一种目标遮挡情况下的自动跟踪控制方法[J].应用光学, 2017, 38(5):713-718. http://www.yygx.net/CN/abstract/abstract11003.shtml LI Chao, WU Yujing, XU Feifei, et al. Auto-tracking control method against target occlusion[J]. Journal of Applied Optics, 2017, 38(5):713-718. http://www.yygx.net/CN/abstract/abstract11003.shtml
[9]	杨亚男.视频目标跟踪的抗遮挡算法研究[D].开封: 河南大学, 2018. http://cdmd.cnki.com.cn/Article/CDMD-10475-1018226859.htm YANG Yanan. Research on anti-occlusion algorithm for video target tracking[D]. Kaifeng: Henan University, 2018. http://cdmd.cnki.com.cn/Article/CDMD-10475-1018226859.htm
[10]	刘万军, 张壮, 姜文涛, 等.遮挡判别下的多尺度相关滤波跟踪算法[J].中国图像图形学报, 2018, 23(12):1789-1800. http://d.old.wanfangdata.com.cn/Periodical/zgtxtxxb-a201812002 LIU Wanjun, ZHANG Zhuang, JIANG Wentao, et al. Multi-scale correlation filter tracking algorithm based on occlusion discrimination[J]. Journal of Image and Graphics, 2018, 23(12):1789-1800. http://d.old.wanfangdata.com.cn/Periodical/zgtxtxxb-a201812002
[11]	PETERSEN W, ISETH O, NORD T S, et al. Estimation of the full-field dynamic response of a floating bridge using Kalman-type filtering algorithms[J]. Mechanical Systems & Signal Processing, 2018, 107:12-28. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=39a47cba9e49b1081d940216af01f313
[12]	ZHU Suguo, DU Junping, REN Nan. A novel simple visual tracking algorithm based on hashing and deep learning[J]. Chinese Journal of Electronics, 2017, 26(5):1073-1078. doi: 10.1049/cje.2016.06.026
[13]	WEI W, LIU X H, ZHOU B B, et al. Sea surface target detection and recognition algorithm based on local and global salient region detection[C]. San Diego: Applications of Digital Image Processing, 2016: 99712U(1-8).
[14]	ZHANG L, LI J H, LU H C. Saliency detection via extreme learning machine[J]. Neurocomputing, 2016, 218: 103-112. doi: 10.1016/j.neucom.2016.08.066
[15]	RAMADAN H, TAIRI H. Robust segmentation of moving objects in video based on spatiotemporal visual saliency and active contour model[J]. Journal of Electronic Imaging, 2016, 25(6): 061612-1-10. doi: 10.1117/1.JEI.25.6.061612

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[7]	CHEN Dong, LIN Jian-lin, MA De-bao. Small electro-optical target detection based on two-scale wavelet analysis[J]. Journal of Applied Optics, 2011, 32(3): 492-497.
[8]	HE Peng-fei, SU Xin-yan, WANG jian. Small target detection based on image sequences[J]. Journal of Applied Optics, 2011, 32(2): 272-275.
[9]	ZHANG Heng, LI You, LI Li-chun, LEI Zhi-hui. Scale-adaptive real-time detection for small targets[J]. Journal of Applied Optics, 2008, 29(1): 9-13.
[10]	CHEN Dong-yan, ZHANG Qi, WANG Yan-ling, LUO Shi-tu. Implementation of maneuver target prediction in image tracking system[J]. Journal of Applied Optics, 2007, 28(1): 33-37.

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Indirect location method for building target based on automatic selection of auxiliary target