• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Chen, Xinqiang (Chen, Xinqiang.) | Wang, Zichuang (Wang, Zichuang.) | Hua, Qiaozhi (Hua, Qiaozhi.) | Shang, Wen-Long (Shang, Wen-Long.) | Luo, Qiang (Luo, Qiang.) | Yu, Keping (Yu, Keping.)

收录:

EI Scopus SCIE

摘要:

Automated container terminal (ACT) is considered as port industry development direction, and accurate kinematic data (speed, volume, etc.) is essential for enhancing ACT operation efficiency and safety. Port surveillance videos provide much useful spatial-temporal information with advantages of easy obtainable, large spatial coverage, etc. In that way, it is of great importance to analyze automated guided vehicle (AGV) trajectory movement from port surveillance videos. Motivated by the newly emerging computer vision and artificial intelligence (AI) techniques, we propose an ensemble framework for extracting vehicle speeds from port-like surveillance videos for the purpose of analyzing AGV moving trajectory. Firstly, the framework exploits vehicle position in each image via a feature-enhanced scale-aware descriptor. Secondly, we match vehicle position and trajectory data from the previous step output via Kalman filter and Hungarian algorithm, and thus we obtain the vehicular imaging trajectory in a frame-by-frame manner. Thirdly, we estimate the vehicular moving speed in real-world via the help of perspective projection theory. The experimental results suggest that our proposed framework can obtain accurate vehicle kinematic data under typical port traffic scenarios considering that the average measurement error of root mean square deviation is 0.675 km/h, the mean absolute deviation is 0.542 km/h, and the Pearson correlation coefficient is 0.9349. The research findings suggest that cutting-edge AI and computer vision techniques can accurately extract on-site vehicular trajectory related data from port videos, and thus help port traffic participants make more reasonable management decisions.

关键词:

Videos Vehicle detection Surveillance Kalman filters feature-enhanced scale-aware detector Feature extraction AI-empowered techniques vehicular trajectory analysis speed data extraction Trajectory Detectors automated container terminal

作者机构:

  • [ 1 ] [Chen, Xinqiang]Shanghai Maritime Univ, Inst Logist Sci & Engn, Shanghai 201306, Peoples R China
  • [ 2 ] [Wang, Zichuang]Shanghai Maritime Univ, Inst Logist Sci & Engn, Shanghai 201306, Peoples R China
  • [ 3 ] [Hua, Qiaozhi]Hubei Univ Arts & Sci, Comp Sch, Xiangyang 441053, Peoples R China
  • [ 4 ] [Shang, Wen-Long]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100021, Peoples R China
  • [ 5 ] [Shang, Wen-Long]Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China
  • [ 6 ] [Shang, Wen-Long]Imperial Coll London, Ctr Transport Studies, London SW7 2BX, England
  • [ 7 ] [Luo, Qiang]Guangzhou Univ, Sch Civil Engn, Guangzhou 510006, Peoples R China
  • [ 8 ] [Yu, Keping]Hosei Univ, Grad Sch Sci & Engn, Tokyo 1848584, Japan

通讯作者信息:

查看成果更多字段

相关关键词:

来源 :

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

ISSN: 1524-9050

年份: 2022

期: 4

卷: 24

页码: 4541-4552

8 . 5

JCR@2022

8 . 5 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:49

JCR分区:1

中科院分区:1

被引次数:

WoS核心集被引频次: 92

SCOPUS被引频次: 97

ESI高被引论文在榜: 7 展开所有

  • 2024-11
  • 2024-11
  • 2024-9
  • 2024-9
  • 2024-7
  • 2024-5
  • 2024-3

万方被引频次:

中文被引频次:

近30日浏览量: 0

归属院系:

在线人数/总访问数:388/4969607
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司