A camouflage target detection method based on local minimum difference constraints

doi:10.23919/JSEE.2022.000049

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (3): 696-705.doi: 10.23919/JSEE.2022.000049

• ELECTRONICS TECHNOLOGY • Previous Articles

A camouflage target detection method based on local minimum difference constraints

Yuanying GAN(), Chuntong LIU(), Hongcai LI(), Zhongye LIU()

¹ School of Missile Engineering, Rocket Force University of Engineering, Xi’an 710025, China

Received:2021-05-25 Online:2023-06-15 Published:2023-06-30
Contact: Yuanying GAN E-mail:yiran_gan@sina.cn;Liuchuntong72@.sina.com;1013312965@qq.com;zhongye_liu81192@163.com
About author:
GAN Yuanying was born in 1993. She received her M.S. degree from Xi’an Shiyou University, Xi’an, China, in 2018. She is currently pursuing her Ph.D. degree with the Rocket Force University of Engineering, China. Her current research interests include image processing and camouflage image analysis. E-mail: yiran_gan@sina.cn

LIU Chuntong was born in 1972. He received his B.S., M.S., and Ph.D degrees in the Second Artillery College of Engineering, Xi’an, China, in 1993, 1996 and 2009, respectively. He is currently with the Rocket Force University of Engineering, China. His major research interests are photoelectric technology and optical fiber sensing technology and application. E-mail: Liuchuntong72@.sina.com

LI Hongcai was born in 1983. He received his B.S., M.S., and Ph.D degrees in the Second Artillery College of Engineering, Xi’an, China, in 2005, 2008 and 2012, respectively. He is currently with the Rocket Force University of Engineering, China. His major research interest is photoelectric protection theory and technology. E-mail: 1013312965@qq.com

LIU Zhongye was born in 1996. He received his B.S. degree from Hebei University of Science and Technology, Shijiazhuang, China, in 2019. He is currently pursuing his M.S. degree with the Rocket Force University of Engineering, China. His current research interests include image processing and image segmentation. E-mail: zhongye_liu81192@163.com

Abstract

Abstract:

To address the problems of missing inside and incomplete edge contours in camouflaged target detection results, we propose a camouflaged moving target detection algorithm based on local minimum difference constraints (LMDC). The algorithm first uses the mean to optimize the initial background model, removes the stable background region by global comparison, and extracts the edge point set in the potential target region so that each boundary point (seed) grows along the center of the target. Finally, we define the minor difference constraints term, combine the seed path and the target space consistency, and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection. The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms. The experimental results show that the proposed algorithm yields good results based on integrity, accuracy, and a number of objective evaluation indexes, and its overall performance is better than that of the compared algorithms.

Key words: camouflage target detection, moving target, local contrast

Yuanying GAN, Chuntong LIU, Hongcai LI, Zhongye LIU. A camouflage target detection method based on local minimum difference constraints[J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 696-705.

Figures/Tables 11

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Indicator	Lab					Sofa
Indicator	ViBe	PSPNet	Siammask	SINet	LMDC	ViBe	PSPNet	Siammask	SINet	LMDC
A_D	0.2217	0.8898	1.0362	1.0508	1.0799	0.9775	1.0435	0.9637	0.9521	1.1046
R	1.4017	0.5450	0.0043	0.0037	0.0066	0.6951	0.2459	0.0049	0.0033	0.0078
MAPE	0.7519	0.9144	0.9349	0.9517	0.9067	0.8508	0.6262	0.5445	0.8632	0.8964
F_measure	0.7207	0.8898	0.9327	0.9201	0.9465	0.6358	0.5053	0.5302	0.6030	0.9478

Indicator	Commando					Jungle
Indicator	ViBe	PSPNet	Siammask	SINet	LMDC	ViBe	PSPNet	Siammask	SINet	LMDC
A_D	0	—	0.5436	1.0173	1.0631	0	—	0.3384	0	0.3179
R	2.7893	—	0.0057	0.0019	0.0012	1.0835	—	0.0082	0.0103	0.0009
MAPE	0.1581	—	0.8014	0.8662	0.8661	0.0518	—	0.3477	0.2018	0.4164
F_measure	0.1846	—	0.8242	0.6404	0.9282	0.1510	—	0.6403	0.4893	0.4152

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A camouflage target detection method based on local minimum difference constraints

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