Volume 15 Issue 2
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2015  >  15(2): 109-117
CUI Hua, ZHANG Xiao, GUO Lu, YUAN Chao, XUE Shi-jiao, SONG Huan-sheng. Cascade AdaBoost pedestrian detector with multi-features and multi-thresholds[J]. Journal of Traffic and Transportation Engineering, 2015, 15(2): 109-117. doi: 10.19818/j.cnki.1671-1637.2015.02.012
Citation: CUI Hua, ZHANG Xiao, GUO Lu, YUAN Chao, XUE Shi-jiao, SONG Huan-sheng. Cascade AdaBoost pedestrian detector with multi-features and multi-thresholds[J]. Journal of Traffic and Transportation Engineering, 2015, 15(2): 109-117. doi: 10.19818/j.cnki.1671-1637.2015.02.012
shu

Cascade AdaBoost pedestrian detector with multi-features and multi-thresholds

doi: 10.19818/j.cnki.1671-1637.2015.02.012

  • School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

More Information
  • Author Bio:

    CUI Hua(1977-), female, associate professor, PhD, +86-29-62630027, huacui@chd.edu.cn

  • Received Date: 2014-11-13
  • Publish Date: 2015-02-25
    Abstract
  • In order to meet the practical demand for pedestrian detection with high speed, high accuracy and strong robustness, in view of the poor quality and unapparent local image features of traffic videos, some simple pedestrian features were chosen for pedestrian detection.Besides rectangle degree, ratio of height to width, shape complexity, normalized width, and pedestrian area, head density was applied because it is a simple local feature and has strong robustness for occlusion interference.Considering the size changing of pedestrian in the image, region division strategy was introduced into image region division.An improved training algorithm based on the minimum principle of classification error and the maximum principle of positive sample classification rate was implemented by considering both high detection rate and low false detection rate, thus several single-feature AdaBoost pedestrian detectors with multi-thresholds were obtained.To optimize the detection performance of cascade pedestrian detectors, the cascade rulewas obtained in term of the contribution rate.The contribution rate was defined by weighing detection performance and detection speed.The cascade order was the detectors based on ratio of height to width, normalized width, rectangle degree, pedestrian area, shape complexity and head density.The pedestrian detectors were sequentially cascaded according to the cascade order, thus a cascade AdaBoost pedestrian detector with multi-features and multi-thresholds was constructed.The proposed pedestrian detector was tested by using 3traffic scenes, and compared with singlecascade-level AdaBoost pedestrian detector and 2existed cascade AdaBoost pedestrian detectors.Analysis result indicates that in 3traffic scenes, compared with the other pedestrain detectors, the proposed pedestrain detector has higher detection rate, higher detection speed and lower false detection rate, the minimum detection rate is 96.70%, the maximum false detection rate is0.67%, and the detection time is less than 5s.So the detector satisfies the real-time and reliable requirements of pedestrian detection in traffic scene.

     

    • FullText(HTML)
  • loading
    • References(24)
  • [1]
    郭立君, 刘曦, 赵杰煜, 等. 结合运动信息与表观特征的行人检测方法[J]. 软件学报, 2012, 23(2): 299-309. https://www.cnki.com.cn/Article/CJFDTOTAL-RJXB201202012.htm

    GUO Li-jun, LIU Xi, ZHAO Jie-yu, et al. Pedestrian detection method of integrated motion information and appearance features[J]. Journal of Software, 2012, 23(2): 299-309. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-RJXB201202012.htm
    [2]
    NEGRI P, GOUSSIES N, LOTITO P. Detecting pedestrians on a movement feature space[J]. Pattern Recognition, 2014, 47(1): 56-71. doi: 10.1016/j.patcog.2013.05.020
    [3]
    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
    [4]
    BORGES P V K. Pedestrian detection based on blob motion statistics[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(2): 224-235. doi: 10.1109/TCSVT.2012.2203217
    [5]
    GAVRILA D M. A Bayesian, exemplar-based approach to hierarchical shape matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(8): 1408-1421. doi: 10.1109/TPAMI.2007.1062
    [6]
    PRIOLETTI A, MØGELMOSE A, GRISLERI P, et al. Part-based pedestrian detection and feature-based tracking for driver assistance: real-time, robust algorithms, and evaluation[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3): 1346-1359. doi: 10.1109/TITS.2013.2262045
    [7]
    ENZWEILER M, GAVRILA D M. Monocular pedestrian detection: survey and experiments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2179-2195.
    [8]
    VÁZQUEZ D, LÓPEZ A M, MARÍN J, et al. Virtual and real world adaptation for pedestrian detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(4): 797-809.
    [9]
    CAO Xian-bin, WANG Zhong, YAN Ping-kun, et al. Transfer learning for pedestrian detection[J]. Neurocomputing, 2013, 100: 51-57.
    [10]
    CHENG Wen-chang, JHAN D M. A self-constructing cascade classifier with AdaBoost and SVM for pedestrian detection[J]. Engineering Applications of Artificial Intelligence, 2013, 26(3): 1016-1028.
    [11]
    汤义. 智能交通系统中基于视频的行人检测与跟踪方法的研究[D]. 广州: 华南理工大学, 2010.

    TANG Yi. Study on video-based detection and tracking method of pedestrian in ITS[D]. Guangzhou: South China University of Technology, 2010. (in Chinese).
    [12]
    DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]∥IEEE. Proceedings of the 2005IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2005: 886-893.
    [13]
    邵枭虎. 基于AdaBoost与显著信息的行人检测算法[D]. 成都: 电子科技大学, 2012.

    SHAO Xiao-hu. The algorithm of pedestrian detection based on AdaBoost and saliency information[D]. Chengdu: University of Electronic Science and Technology of China, 2012. (in Chinese).
    [14]
    MEYNET J, POPOVICI V, THIRAN J P. Face detection with boosted Gaussian features[J]. Pattern Recognition, 2007, 40(8): 2283-2291.
    [15]
    TIAN Hong, DUAN Zhu, ABRAHAM A, et al. A novel multiplex cascade classifier for pedestrian detection[J]. Pattern Recognition Letters, 2013, 34(14): 1687-1693.
    [16]
    常峰, 杨彬, 窦建华. 基于多特征和级联分类器的行人检测算法[J]. 合肥工业大学学报: 自然科学版, 2014, 37(12): 1456-1461. https://www.cnki.com.cn/Article/CJFDTOTAL-HEFE201412010.htm

    CHANG Feng, YANG Bin, DOU Jian-hua. Pedestrian detection algorithm with multiple feature and cascade classifier[J]. Journal of Hefei University of Technology: Natural Science, 2014, 37(12): 1456-1461. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEFE201412010.htm
    [17]
    李梦涵, 熊淑华, 熊文, 等. 多尺度级联行人检测算法的研究与实现[J]. 计算机技术与发展, 2014, 24(8): 10-13. https://www.cnki.com.cn/Article/CJFDTOTAL-WJFZ201408003.htm

    LI Meng-han, XIONG Shu-hua, XIONG Wen, et al. Research and realization of pedestrian detection algorithm by multi-scale cascaded features[J]. Computer Technology and Development, 2014, 24(8): 10-13. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WJFZ201408003.htm
    [18]
    程如中, 赵勇, 王执中, 等. 实时行人检测预警系统[J]. 交通运输工程学报, 2012, 12(5): 110-118, 126. http://transport.chd.edu.cn/article/id/201205015

    CHENG Ru-zhong, ZHAO Yong, WONG Chup-chung, et al. Real-time pedestrian detecting and warning system[J]. Journal of Traffic and Transportation Engineering, 2012, 12(5): 110-118, 126. (in Chinese). http://transport.chd.edu.cn/article/id/201205015
    [19]
    FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to Boosting[J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139.
    [20]
    SCHAPIRE R E, SINGER Y. Improved boosting algorithms using confidence-rated predictions[J]. Machine Learning, 1999, 37(3): 297-336.
    [21]
    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.
    [22]
    DENG Jin-hao, ZHU Juan. Research on pedestrian detection algorithms based on video[C]∥IEEE. 2010IEEE Conference on Computer Design and Applications. New York: IEEE, 2010: 474-478.
    [23]
    LEE Y D, LI Z Z, ZHANG S R, et al. Safety impacts of red light running photo enforcement at urban signalized intersections[J]. Journal of Traffic and Transportation Engineering: English Edition, 2014, 1(5): 309-324.
    [24]
    LIU Qing-hua, CHUNG E, ZHAI Liu-jia. Fusing moving average model and stationary wavelet decomposition for automatic incident detection: case study of Tokyo expressway[J]. Journal of Traffic and Transportation Engineering: English Edition, 2014, 1(6): 404-414.
    • 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 (739) PDF downloads(901) 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