Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions Articles uri icon

publication date

  • September 2010

start page

  • 8028

end page

  • 8053

issue

  • 9

volume

  • 10

International Standard Serial Number (ISSN)

  • 1424-8220

abstract

  • The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time
    algorithms to send the proper, accurate messages to the drivers. In this
    article, an application to detect and predict the movement of
    pedestrians in order to prevent an imminent collision has been developed
    and tested under real conditions. The proposed application, first,
    accurately measures the position of obstacles using a two-sensor hybrid
    fusion approach: a stereo camera vision system and a laser scanner.
    Second, it correctly identifies pedestrians using intelligent algorithms
    based on polylines and pattern recognition related to leg positions
    (laser subsystem) and dense disparity maps and u-v disparity (vision
    subsystem). Third, it uses statistical validation gates and confidence
    regions to track the pedestrian within the detection zones of the
    sensors and predict their position in the upcoming frames. The
    intelligent sensor application has been experimentally tested with
    success while tracking pedestrians that cross and move in zigzag fashion
    in front of a vehicle.