Real-time pedestrian detection and pose classification on a GPU

In this contribution, the authors present a real-time pedestrian detection and pose classification system which makes use of the computing power of Graphical Processing Units (GPUs). The aim of the pose classification presented here is to determine the orientation and thus the likely future movement of the pedestrian. The authors focus on the evaluation of pose detection performance and show that, without resorting to complex tracking or attention mechanisms, a small number of safety-relevant pedestrian poses can be reliably distinguished during live operation. Additionally, the authors show that detection and pose classification can share the same visual low-level features, achieving a very high frame rate at high image resolutions using only off-the-shelf hardware.

• Record URL:
  https://doi.org/10.1109/ITSC.2013.6728256
• Availability:
  • Find a library where document is available. Order URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6712176
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Corporate Authors:

  Institute of Electrical and Electronics Engineers (IEEE)

  3 Park Avenue, 17th Floor
  New York, NY  United States  10016-5997
• Authors:
  • Gepperth, Alexander
  • Ortiz, Michael Garcia
  • Heisele, Bernd
• Conference:
  • 16th International IEEE Conference on Intelligent Transportation Systems (ITSC)
  • Location: The Hague , Netherlands
  • Date: 2013-10-6 to 2013-10-9
• Publication Date: 2013-10

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Photos; References;
• Pagination: pp 348-353
• Monograph Title: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

Subject/Index Terms

• TRT Terms: Detection and identification; Image analysis; Pedestrian detectors; Pedestrians
• Uncontrolled Terms: Graphics processing units
• Subject Areas: Pedestrians and Bicyclists; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01564820
• Record Type: Publication
• ISBN: 9781479929146
• Files: TRIS
• Created Date: May 28 2015 9:06AM