Pedestrian Collision Warning Systems Using Neural Networks Based on a Single Camera

This paper proposes a method of achieving fast detection of pedestrians, while simultaneously maintaining good performance regardless of variation in illumination, and in both shape and scale of pedestrians with a single camera. Regions of interest (ROIs) are acquired by optical flow fields using the Lucas-Kanade algorithm, and classified by convolutional neural networks (CNNs) whether they are pedestrians. Detected pedestrians are tracked by using a particle filter based on adaptive fusion frameworks. The CNNs allow the proposed system to be robust to variation in illumination and in both shape and scale of pedestrians; and proposed methods of setting ROIs and tracking pedestrians allow this system to detect a dangerous situation and warn it to a driver fast. A single camera is only used to conduct this method, thus, the proposed system is also economically efficient.

Language

  • English

Media Info

  • Media Type: CD-ROM
  • Features: Figures; Photos; References; Tables;
  • Pagination: 12p
  • Monograph Title: ITS Connections: Saving Time. Saving Lives

Subject/Index Terms

Filing Info

  • Accession Number: 01144967
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 17 2009 9:21AM