ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Original Paper

Research on flow-limiting facility optimization in rail transit stations based on optical feature descriptor

  • School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.04.010
  • Received Date: 08 January 2018
  • Rev Recd Date: 11 April 2018
  • Publish Date: 30 April 2018
    Abstract

  • Abstract

    To address the problem of low intelligence and flexibility of existing flow limiting facilities, a new optimization method for flow-limiting facilities in rail transit stations based on optical feature descriptors, is proposed. First, the region of interest (ROI) is set according to the scene characteristics of rail transit stations to reduce the computation of subsequent operation. Then, the features of image sequence are analyzed by establishing optical feature descriptors. Finally, the one-class SVM is adjusted according to the clumped features of pedestrians to make condition detection possible. Experimental results demonstrate that the proposed method can detect the overload status accurately, improve the automatic level of flow-limiting facilities effectively, and provides data support and theoretical basis for organization and management of pedestrians in rail transit stations.

    Abstract

    To address the problem of low intelligence and flexibility of existing flow limiting facilities, a new optimization method for flow-limiting facilities in rail transit stations based on optical feature descriptors, is proposed. First, the region of interest (ROI) is set according to the scene characteristics of rail transit stations to reduce the computation of subsequent operation. Then, the features of image sequence are analyzed by establishing optical feature descriptors. Finally, the one-class SVM is adjusted according to the clumped features of pedestrians to make condition detection possible. Experimental results demonstrate that the proposed method can detect the overload status accurately, improve the automatic level of flow-limiting facilities effectively, and provides data support and theoretical basis for organization and management of pedestrians in rail transit stations.
    • FullText(HTML)
  • loading
    • References(18)
  • [1]
    尹来盛, 张明丽. 我国超大城市交通拥堵及其治理对策研究—以广州市为例[J]. 城市观察, 2017, 1(2): 73-84.
    [2]
    胡亚楠. 地铁屏蔽门、车门夹人成因分析及防范措施研究[J]. 科技创新与应用, 2017, (10):82-83.
    [3]
    朱正玲. 城市轨道交通网络高峰时段常态限流问题研究[J]. 铁路通信信号工程技术, 2017, 14(02) :71-74.
    [4]
    肖慧雅,姚丽亚,曾伟,等. 城市轨道交通站点限流设施优化方法研究[J]. 道路交通与安全, 2016, 16(6): 46-50.
    [5]
    赵晓旭. 广州地铁换乘车站节假日客流组织与管控研究[J]. 科技资讯, 2015, 13(6): 211-212.
    [6]
    吴君尚, 张碧纯, 胡湲,等.轨交车站站外限流栏杆设置方案优化研究[J].地下工程与隧道, 2013, (1): 38-41.
    [7]
    张月坤. 北京地铁客流密度自动检测技术研究[J]. 中国铁路, 2017, (4): 96-100.
    [8]
    张月坤. 北京地铁视频监测客流告警阈值的针对性和准确性研究[J]. 中国铁路, 2017, (5):78-81.
    [9]
    赵保锋,邹晓磊,屈晓宜.基于仿真的城市轨道交通站台客流滞留分级预警方法[J].城市轨道交通研究, 2017, 20(9): 107-110,115.
    [10]
    WANG T, SNOUSSI H. Detection of abnormal visual events via global optical flow orientation histogram[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(6): 988-998.
    [11]
    LI A, MIAO Z J, CEN Y G, et al. Histogram of maximal optical flow projection for abnormal events detection in crowded scenes[J]. International Journal of Distributed Sensor Networks, 2015, 11(6): 1-11.
    [12]
    YI Y G, LI X H, ZHAO R, et al. A constrained sparse representation approach for video anomaly detection[C]// Advanced Information Management, Communicates, Electronic and Automation Control Conference. Xi’an, China: IEEE Press, 2016: 45-49.
    [13]
    CHEN L X, GUO H W, WU X Y, et al. Detecting anomaly based on time dependence for large scenes[C]// 2016 IEEE International Conference on Information and Automation. Piscataway, NJ, USA: IEEE Press, 2016: 1376-1381.
    [14]
    ZHAO Y, ZHOU L, FU K, et al. Abnormal event detection using spatio-temporal feature and nonnegative locality-constrained linear coding[C]// International Conference on Image Processing. Piscataway, USA: IEEE Press, 2016: 3354-3358.
    [15]
    WU F, JING X Y, LIU Q, et al. Large-scale image recognition based on parallel kernel supervised and semi-supervised subspace learning[J]. Neural Computing and Applications, 2017, 28(3): 483-498.
    [16]
    XIAN Y, RONG X, YANG X, et al. Evaluation of Low-Level Features for Real-World Surveillance Event Detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(3): 624-634.
    [17]
    张强,王正林. 精通Matlab图像处理[M]. 电子工业出版社, 2009.
    [18]
    王爱丽, 董宝田, 王泽胜, 等. 融合光流速度场自适应背景建模的交通场景中运动行人检测算法[J]. 长安大学学报, 2015, 1, 35(S): 184-188.
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    Get Citation
    PDF
    XML
    [1]
    尹来盛, 张明丽. 我国超大城市交通拥堵及其治理对策研究—以广州市为例[J]. 城市观察, 2017, 1(2): 73-84.
    [2]
    胡亚楠. 地铁屏蔽门、车门夹人成因分析及防范措施研究[J]. 科技创新与应用, 2017, (10):82-83.
    [3]
    朱正玲. 城市轨道交通网络高峰时段常态限流问题研究[J]. 铁路通信信号工程技术, 2017, 14(02) :71-74.
    [4]
    肖慧雅,姚丽亚,曾伟,等. 城市轨道交通站点限流设施优化方法研究[J]. 道路交通与安全, 2016, 16(6): 46-50.
    [5]
    赵晓旭. 广州地铁换乘车站节假日客流组织与管控研究[J]. 科技资讯, 2015, 13(6): 211-212.
    [6]
    吴君尚, 张碧纯, 胡湲,等.轨交车站站外限流栏杆设置方案优化研究[J].地下工程与隧道, 2013, (1): 38-41.
    [7]
    张月坤. 北京地铁客流密度自动检测技术研究[J]. 中国铁路, 2017, (4): 96-100.
    [8]
    张月坤. 北京地铁视频监测客流告警阈值的针对性和准确性研究[J]. 中国铁路, 2017, (5):78-81.
    [9]
    赵保锋,邹晓磊,屈晓宜.基于仿真的城市轨道交通站台客流滞留分级预警方法[J].城市轨道交通研究, 2017, 20(9): 107-110,115.
    [10]
    WANG T, SNOUSSI H. Detection of abnormal visual events via global optical flow orientation histogram[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(6): 988-998.
    [11]
    LI A, MIAO Z J, CEN Y G, et al. Histogram of maximal optical flow projection for abnormal events detection in crowded scenes[J]. International Journal of Distributed Sensor Networks, 2015, 11(6): 1-11.
    [12]
    YI Y G, LI X H, ZHAO R, et al. A constrained sparse representation approach for video anomaly detection[C]// Advanced Information Management, Communicates, Electronic and Automation Control Conference. Xi’an, China: IEEE Press, 2016: 45-49.
    [13]
    CHEN L X, GUO H W, WU X Y, et al. Detecting anomaly based on time dependence for large scenes[C]// 2016 IEEE International Conference on Information and Automation. Piscataway, NJ, USA: IEEE Press, 2016: 1376-1381.
    [14]
    ZHAO Y, ZHOU L, FU K, et al. Abnormal event detection using spatio-temporal feature and nonnegative locality-constrained linear coding[C]// International Conference on Image Processing. Piscataway, USA: IEEE Press, 2016: 3354-3358.
    [15]
    WU F, JING X Y, LIU Q, et al. Large-scale image recognition based on parallel kernel supervised and semi-supervised subspace learning[J]. Neural Computing and Applications, 2017, 28(3): 483-498.
    [16]
    XIAN Y, RONG X, YANG X, et al. Evaluation of Low-Level Features for Real-World Surveillance Event Detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(3): 624-634.
    [17]
    张强,王正林. 精通Matlab图像处理[M]. 电子工业出版社, 2009.
    [18]
    王爱丽, 董宝田, 王泽胜, 等. 融合光流速度场自适应背景建模的交通场景中运动行人检测算法[J]. 长安大学学报, 2015, 1, 35(S): 184-188.
    Recommended articles
    TrendMD
    Volume 48 Issue 4 page: 341-346
    Cover

    Article Metrics

    Article views (758) PDF downloads(373)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return