Computer Science ›› 2015, Vol. 42 ›› Issue (8): 86-89.

Previous Articles     Next Articles

Moving Target Detection Using Fusion of Visual and Thermal Video

ZHANG Sheng, YAN Yun-yang and LI Yu-feng   

  • Online:2018-11-14 Published:2018-11-14

Abstract: In outdoor environments,visible light camera can get rich texture and spectral information in the scene,but they are greatly influenced by illumination changes.On the contrary,thermal infrared camera is not sensitive to light and it is still able to work effectively under night.But the thermal infrared images have less color information and lower contrast.In order to make full use of the complementary information of infrared and visible light for detection target,a novel method based on Gaussian mixture model with RGBT was proposed for moving target detection more accurately and robust.This method adds the thermal infrared images as the fourth component to the conventional Gaussian mixture model to improve the positive detection rate.Meanwhile,the shadow removal algorithm is introduced to reduce the impact of shadows caused by the ambient illumination changes,so the robustness of proposed method is enhanced.Experimental results show that the suggested method not only achieves the higher detection accuracy and more complete object,but also meets the real-time requirements better compared to the conventional Gaussian mixture models.

Key words: Moving target detection,Thermal video,Visible video,Data fusion,Gaussian mixture model

[1] 张笙,李郁峰,严云洋,等.面向鲁棒视觉监控的热红外与可见光视频融合运动目标检测[J].红外技术,2013(12):773-779 Zhang Sheng,Li Yu-feng,Yan Yun-yang,et al.Moving Target Detection Using Fusion of Visual and Thermal Video for Robust Surveillance[J].Infrared Technology,2013(12):773-779
[2] Zhu Z,Huang T S.Multimodal surveillance:an introduction[C]∥Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2007).Washington DC,USA:IEEE Computer Society,2007:1-6
[3] Davis J W,Sharma V.Fusion-based background-subtractionusing contour saliency[C]∥Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Re-cognition(CVPR 2005).Washington DC,USA:IEEE Computer Society,2005:11-18
[4] Torresan H,Turgeon B,Ibarra-Castanedo C,et al.Advanced surveillance systems:combining video and thermal imagery for pedestrian detection[C]∥Defense and Security,International Society for Optics and Photonics.Bellingham,USA:SPIE,2004:506-515
[5] Snidaro L,Foresti G L,Niu R,et al.Sensor fusion for video surveillance[C]∥Proceedings of the 7th International Conference on Information Fusion.New York,USA:Electrical Engineering and Computer Science,2004:739-746
[6] Stauffer C,Grimson W E L.Learning patterns of activity using real-time tracking [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):747-757
[7] 黄文丽,范勇,李绘卓,等.改进的混合高斯算法[J].计算机工程与设计,2011,32(2):592-595 Huang Wei-li,Fan Yong,Li Hui-zhuo,et al.Improved Mixture Gaussian Algorithm[J].Computer Engineering and Design,2011,2(2):592-595
[8] 姚会,苏松志,王丽,等.基于改进的混合高斯模型的运动目标检测方法[J].厦门大学学报(自然科学版),2008,47(4):505-510 Yao Hui,Su Song-zhi,Wang Li,et al.Moving Object Dection Method Based on the Improved Mixture Model[J].Journal of Xiamen University(Natural Science),2008,47(4):505-510
[9] 李鸿.基于混合高斯模型的运动检测及阴影消除算法研究[D].天津:中国民航大学,2013 Li Hong.The Research of Motion Detection Based on Gaussian Mixture Model and Shadow Elimination Algorithm[D].Tianjin:Civil Aviation University of China,2013

No related articles found!
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .