A Unified Framework for Concurrent Pedestrian and Cyclist Detection

Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, the authors present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.

• Record URL:
  • https://doi.org/10.1109/TITS.2016.2567418
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2017, IEEE.
• Authors:
  • Li, Xiaofei
  • Li, Lingxi
  • Flohr, Fabian
  • Wang, Jianqiang
  • Xiong, Hui
  • Bernhard, Morys
  • Pan, Shuyue
  • Gavrila, Dariu M
  • Li, Keqiang
• Publication Date: 2017-2

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 269-281
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 18
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Computer vision; Cyclists; Detection and identification systems; Driver support systems; Image analysis; Pedestrians; Traffic safety
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Safety and Human Factors;

Filing Info

• Accession Number: 01628032
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Mar 1 2017 9:05AM