Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation

Detecting pedestrians is still a challenging task for automotive vision systems due to the extreme variability of targets, lighting conditions, occlusion, and high-speed vehicle motion. Much research has been focused on this problem in the last ten years and detectors based on classifiers have gained a special place among the different approaches presented. This paper presents a state-of-the-art pedestrian detection system based on a two-stage classifier. Candidates are extracted with a Haar cascade classifier trained with the Daimler Detection Benchmark data set and then validated through a part-based histogram-of-oriented-gradient (HOG) classifier with the aim of lowering the number of false positives. The surviving candidates are then filtered with feature-based tracking to enhance the recognition robustness and improve the results' stability. The system has been implemented on a prototype vehicle and offers high performance in terms of several metrics, such as detection rate, false positives per hour, and frame rate. The novelty of this system relies on the combination of a HOG part-based approach, tracking based on a specific optimized feature, and porting on a real prototype.

• Record URL:
  • https://doi.org/10.1109/TITS.2013.2262045
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • Prioletti, Antonio
  • Møgelmose, Andreas
  • Grisleri, Paolo
  • Trivedi, Mohan Manubhai
  • Broggi, Alberto
  • Moeslund, Thomas B
• Publication Date: 2013-9

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1346-1359
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 14
  • Issue Number: 3
  • 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; Classification; Detection and identification systems; Driver support systems; Pedestrians
• Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; I71: Traffic Theory; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01524677
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: May 1 2014 4:36PM