DeepStep: Direct Detection of Walking Pedestrian From Motion by a Vehicle Camera

Pedestrian detection has wide applications in intelligent transportation. It is essential to understand pedestrian’s position and action instantaneously for autonomous driving. Most algorithms divide these tasks into sequential procedures where pedestrians are detected from shape-based features in video frames, and their behaviors are analyzed with frame tracking. Different from those, this work introduces a deep learning-based pedestrian detection method that only uses motion cues. The pedestrian motion, which is much different from that of static background and dynamic vehicles, is investigated in the spatial-temporal domain. The pedestrian leg movement forms a chain-type trace in the motion profile images even if the ego-vehicle moves. Instead of modeling walking actions based on kinematics, the chain structure is directly learned from a large pedestrian dataset in driving videos. This method works for the more challenging scenes observed on moving vehicles than those scenes from static cameras. The aim is to detect not only pedestrians promptly but also predict their walking direction in the driving space. Since a video is reduced to temporal images, real-time performance is achieved with a high mean average precision and a low false-positive rate on a publicly available dataset.

• Record URL:
  • https://doi.org/10.1109/TIV.2022.3186962
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Kilicarslan, Mehmet
  • Zheng, Jiang Yu
• Publication Date: 2023-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1652-1663
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 8
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Cameras; Machine learning; Motion; Motor vehicles; Pedestrian detectors; Pedestrian movement; Pedestrians; Walking
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Vehicles and Equipment;

Filing Info

• Accession Number: 01884276
• Record Type: Publication
• Files: TRIS
• Created Date: May 31 2023 10:58AM