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ZHAO Shuang, CHEN Shuyue, WANG Qiaoyue. Infrared Pedestrian Detection in Complex Night Scenes[J]. Infrared Technology , 2021, 43(6): 575-582.
Citation: ZHAO Shuang, CHEN Shuyue, WANG Qiaoyue. Infrared Pedestrian Detection in Complex Night Scenes[J]. Infrared Technology , 2021, 43(6): 575-582.
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Infrared Pedestrian Detection in Complex Night Scenes

  • School of Information Science and Engineering, Changzhou University, Changzhou 213164, China

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  • Received Date: July 01, 2019
  • Revised Date: November 24, 2019
    Graphical Abstract
    • Abstract
  • An infrared pedestrian detection algorithm is proposed to solve the problem of small differences between pedestrians and backgrounds in gray scale images and the occurrence of occlusion in infrared images at night. First, a significant graph with the full coverage of the target is generated by the pedestrian semantic fusion method, and the region of interest is obtained by combining it with the original graph. Then, a two-branch classifier based on the improved histogram of the gradient feature is constructed. The fuzzy score of the classifier is used to determine the occurrence of occlusion and call the head template for the final detection. Experiments based on the LSI far infrared pedestrian dataset and independent datasets of pedestrians captured at night in winter and summer prove that the proposed method is robust and quick in detecting pedestrians under different environments. It can significantly reduce the rate of missed detection and realize a detection rate of 94.20%.
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    • References (18)
  • [1]
    XU Z, ZHUANG J, LIU Q, et al. Nighttime FIR pedestrian detection benchmark dataset for ADAS[C]// Proceedings of Pattern Recognition and Computer Vision, 2018: 323-333.
    [2]
    TAO Y, FU D, SHU P. Pedestrian tracking for infrared image sequence based on trajectory manifold of spatio-temporal slice[J]. Multimedia Tools and Applications, 2017, 76: 11021-11035. DOI: 10.1007/s11042-016-3461-8
    [3]
    刘洋. 基于LS-DYNA的汽车正面碰撞计算机模拟仿真[D]. 西安: 西华大学, 2011.

    LIU Yang. Simulation on the Front Impact of Vehicle Based on LS-DYNA[D]. Xi'an: Xihua University, 2011.
    [4]
    Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection[C]// 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, 1(1): 886-893.
    [5]
    Bosch A, Zisserman A, Munoz X. Representing shape with a spatial pyramid kernel[C]//Acm International Conference on Image & Video Retrieval, 2007: 401-408(doi: https://doi.org/10.1145/1282280.1282340).
    [6]
    Sangeetha D, Deepa P. A low-cost and high-performance architecture for robust human detection using histogram of edge oriented gradients[J]. Microprocessors and Microsystems, 2017, 53: 106-119. DOI: 10.1016/j.micpro.2017.07.009
    [7]
    ZHENG C H, PEI W J, YAN Q, et al. Pedestrian detection based on gradient and texture feature integration[J]. Neurocomputing, 2017, 228: 71-78. DOI: 10.1016/j.neucom.2016.09.085
    [8]
    朱聪聪, 项志宇. 基于梯度方向和强度直方图的红外行人检测[J]. 计算机工程, 2014, 40(12): 195-198, 204. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC201412037.htm

    ZHU Congcong, XIANG Zhiyu. Infrared pedestrian detection based on histograms of oriented gradients and intensity[J]. Computer Engineering, 2014, 40(12): 195-198, 204. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC201412037.htm
    [9]
    Itti L, Koch E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Trans., 1998, 20(11): 1254-1259 http://dl.acm.org/citation.cfm?id=297870
    [10]
    Radhakrishna A, Sheila H, Francisco E, et al. Frequency-tuned salient region detection[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009: (doi: 10.1109/CVPR.2009.5206596).
    [11]
    HOU X D, ZHANG L Q. Saliency detection: a spectral residual approach[C]//2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007: (doi: 10.1109/CVPR.2007.383267).
    [12]
    Alexe B, Deselaers T, Ferrari V. Measuring the objectness of image windows[J]. IEEE Transactions on Software Engineering, 2012, 34(11): 2189-2202. http://newmed.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM22248633
    [13]
    WANG X, HAN T X, YAN S. An HOG-LBP human detector with partial occlusion handling[C]//12th International Conference on Computer Vision of IEEE, 2010: (doi: 10.1109/ICCV.2009.5459207).
    [14]
    Javier M N, Vazquez D, Lopez A M, et al. Occlusion handling via random subspace classifiers for human detection[J]. IEEE sTransactions on Cybernetics, 2013, 44(3): V342-354. http://www.ncbi.nlm.nih.gov/pubmed/23757554
    [15]
    Broggi A, Bertozzi M, Fascioli A, et al. Shape-based pedestrian detection[C]//IEEE Intelligent Vehicles Symposium, 2000: (doi: 10.1109/IVS.2000.898344).
    [16]
    Brehar R, Vancea C, Nedevschi S. Pedestrian detection in infrared images using aggregated channel features[C]//IEEE International Conference on Intelligent Computer Communication & Processing, 2014: (doi: 10.1109/ICCP.2014.p6936964).
    [17]
    LIN T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 99: 2999-3007. DOI: 10.1109/ICCV.2017.324
    [18]
    车凯, 向郑涛, 陈宇峰, 等. 基于改进Fast R-CNN的红外图像行人检测研究[J]. 红外技术, 2018, 40(6): 578-584. http://hwjs.nvir.cn/article/id/hwjs201806010

    CHE Kai, XIANG Zhengtao, CHEN Yufeng, et al. Research on infrared image pedestrian detection based on improved fast R-CNN[J]. Infrared Technology, 2018, 40(6): 578-584. http://hwjs.nvir.cn/article/id/hwjs201806010
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