Volume 16 Issue 6
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2016  >  16(6): 132-139
GUO Ai-ying, XU Mei-hua, RAN Feng, WANG Qi. Model of real-time pedestrian detection under vehicle environment based on CS-SD[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 132-139.
Citation: GUO Ai-ying, XU Mei-hua, RAN Feng, WANG Qi. Model of real-time pedestrian detection under vehicle environment based on CS-SD[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 132-139.
shu

Model of real-time pedestrian detection under vehicle environment based on CS-SD

  • 1.

    School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China

  • 2.

    Department of Mechatronic Engineering, Shanxi Vocational and Technical College of Light Industry, Taiyuan 030013, Shanxi, China

  • 3.

    Microelectronics Research and Development Center, Shanghai University, Shanghai 200072, China

More Information
  • Author Bio:

    GUO Ai-ying(1984-), female, lecturer, doctoral student, +86-21-56331632, gayshh@shu.edu.cn

    XU Mei-hua(1957-), female, professor, PhD, +86-21-56331632, mhxu@shu.edu.cn

  • Received Date: 2016-05-21
  • Publish Date: 2016-12-25
    Abstract
  • In order to solve the real-time problem in the advanced driver assistant system, a model of pedestrian detection based on the calibration of side-of-pavement line and saliency texture detection (CS-SD) and the location histogram of oriented gradient (L-HOG) was proposed.The CS-SD algorithm was used instead of exhaustive search to quickly mark pedestrian area in the image.The L-HOG was used to quickly extract pedestrian feature, and additive kernel support vector machine (AK-SVM) was used to efficiently classify detected objects.Analysis result shows that when 500 images including 832 pedestrians on personal computer are detected, the model detects 720 pedestrians correctly, the detection rate is 86.5%, the error rate is 4.1%, and the detection time is 39 ms.When 48 400 images including 988 pedestrians on vehicle pedestrian detection system based on BF609 are detected, the model detects 861 pedestrians correctly, misses 127 pedestrians and detects 13 pedestrians in error.The detection speed is 20 fps.Underthe premise of not reducing the detection rate, the proposed pedestrian detection model can reach satisfying detection speed and can be used in vehicle equipment of real-time pedestrian detection.

     

    • FullText(HTML)
  • loading
    • References(25)
  • [1]
    BENENSON R, OMRAN M, HOSANG J, et al. Ten years of pedestrian detection, what have we learned?[J]. Lecture Notes in Computer Science, 2015, 8926: 613-627.
    [2]
    DIXIT R S, GANDHE S T. Pedestrian detection system for ADAS using Friendly ARM[C]//IEEE. 2015International Conference on Energy Systems and Applications. New York: IEEE, 2015: 557-560.
    [3]
    GERÓNIMO D, LÓPEZ A M, SAPPA A D, et al. Survey of pedestrian detection for advanced driver assistance systems[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(7): 1239-1258. doi: 10.1109/TPAMI.2009.122
    [4]
    HAJEK W, GAPONOVA I, FLEISCHER K H, et al. Workloadadaptive cruise control—a new generation of advanced driver assistance systems[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2013, 20: 108-120. doi: 10.1016/j.trf.2013.06.001
    [5]
    DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//IEEE. 2005IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2005: 886-893.
    [6]
    WANG Xiao-yu, HAN T X, YAN Shui-cheng. An HOGLBP human detector with partial occlusion handling[C]//IEEE. 2009 IEEE International Conference on Computer Vision. New York: IEEE, 2009: 32-39.
    [7]
    FELZENSZWALB P, GIRSHICK R, MCALLESTER D, et al. Visual object detection with deformable part models[J]. Communications of the ACM, 2013, 56(9): 97-105. doi: 10.1145/2494532
    [8]
    FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained partbased models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645. doi: 10.1109/TPAMI.2009.167
    [9]
    OUYANG Wan-li, ZENG Xing-xu, WANG Xiao-gang. Singlepedestrian detection aided by two-pedestrian detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1875-1889. doi: 10.1109/TPAMI.2014.2377734
    [10]
    DOLLÁR P, WOJEK C, SCHIELE B, et al. Pedestrian detection: an evaluation of the state of the art[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4): 743-761. doi: 10.1109/TPAMI.2011.155
    [11]
    FELZENSZWALB P, MCALLESTER D, RAMANAN D. A discriminatively trained, multiscale, deformable part model[C]//IEEE. 2008 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2008: 1-8.
    [12]
    ZHANG Xiao-wei, HU Hai-miao, JIANG Fan, et al. Pedestrian detection based on hierarchical co-occurrence model for occlusion handling[J]. Neurocomputing, 2015, 168: 861-870. doi: 10.1016/j.neucom.2015.05.038
    [13]
    NEHANIV C L, DAUTENHAHN K, KUBACKI J, et al. A methodological approach relating the classification of gesture to identification of human intent in the context of humanrobot interaction[C]//IEEE. 2005IEEE International Workshop on Robots and Human Interactive Communication. New York: IEEE, 2005: 371-377.
    [14]
    CHO H, RYBSKI P E, BAR-HILLEL A, et al. Real-time pedestrian detection with deformable part models[C]//IEEE. 2012Intelligent Vehicles Symposium. New York: IEEE, 2012: 1035-1042.
    [15]
    CHEN Xiao-feng, HENRICKSON K, WANG Yin-hai. Kinectbased pedestrian detection for crowded scenes[J]. ComputerAided Civil and Infrastructure Engineering, 2016, 31(3): 229-240. doi: 10.1111/mice.12163
    [16]
    CHENG Hong, ZHENG Nan-ning, QIN Jun-jie. Pedestrian detection using sparse Gabor filter and support vector machine[C]//IEEE. 2005Intelligent Vehicles Symposium. New York: IEEE, 2005: 583-587.
    [17]
    WU Si, LAGANIRE R, PAYEUR P. Improving pedestrian detection with selective gradient self-similarity feature[J]. Pattern Recognition, 2015, 48(8): 2364-2376. doi: 10.1016/j.patcog.2015.01.005
    [18]
    ZHANG Shan-shan, BENENSON R, SCHIELE B. Filtered channel features for pedestrian detection[C]//IEEE. 2015IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2015: 1751-1760.
    [19]
    TIAN Yong-long, LUO Ping, WANG Xiao-gang, et al. Deep learning strong parts for pedestrian detection[C]//IEEE. 2015IEEE International Conference on Computer Vision. New York: IEEE, 2015: 1904-1912.
    [20]
    UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2): 154-171. doi: 10.1007/s11263-013-0620-5
    [21]
    DEMIR B, BRUZZONE L. Fast and accurate image classification with histogram based features and additive kernel SVM[C]//IEEE. 2015 IEEE International Geoscience and Remote Sensing Symposium. New York: IEEE, 2015: 2350-2353.
    [22]
    MAJI S, BERG A C, MALIK J. Efficient classification for additive kernel SVMs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 66-77. doi: 10.1109/TPAMI.2012.62
    [23]
    孙锐, 陈军, 高隽. 基于显著性检测与HOG-NMF特征的快速行人检测方法[J]. 电子与信息学报, 2013, 35(8): 1921-1926. https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201308023.htm

    SUN Rui, CHEN Jun, GAO Jun. Fast pedestrian detection based on saliency detection and HOG-NMF features[J]. Journal of Electronics and Information Technology, 2013, 35(8): 1921-1926. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201308023.htm
    [24]
    WU Jian-xin, GEYER C, REHG J M. Real-time human detection using contour cues[C]//IEEE. 2011IEEE International Conference on Robotics and Automation. New York: IEEE, 2011: 860-867.
    [25]
    曾波波, 王贵锦, 林行刚. 基于颜色自相似度特征的实时行人检测[J]. 清华大学学报: 自然科学版, 2012, 52(4): 571-574. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201204030.htm

    ZENG Bo-bo, WANG Gui-jin, LIN Xing-gang. Color selfsimilarity feature based real-time pedestrian detection[J]. Journal of Tsinghua University: Science and Technology, 2012, 52(4): 571-574. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201204030.htm
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (3258) PDF downloads(2750) Cited by()
    Proportional views
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return