• DocumentCode
    1892258
  • Title

    Trajectory analysis and prediction for improved pedestrian safety: Integrated framework and evaluations

  • Author

    Mogelmose, Andreas ; Trivedi, Mohan M. ; Moeslund, Thomas B.

  • Author_Institution
    Visual Anal. of People Lab., Aalborg Univ., Aalborg, Denmark
  • fYear
    2015
  • fDate
    June 28 2015-July 1 2015
  • Firstpage
    330
  • Lastpage
    335
  • Abstract
    This paper presents a monocular and purely vision based pedestrian trajectory tracking and prediction framework with integrated map-based hazard inference. In Advanced Driver Assistance systems research, a lot of effort has been put into pedestrian detection over the last decade, and several pedestrian detection systems are indeed showing impressive results. Considerably less effort has been put into processing the detections further. We present a tracking system for pedestrians, which based on detection bounding boxes tracks pedestrians and is able to predict their positions in the near future. The tracking system is combined with a module which, based on the car´s GPS position acquires a map and uses the road information in the map to know where the car can drive. Then the system warns the driver about pedestrians at risk, by combining the information about hazardous areas for pedestrians with a probabilistic position prediction for all observed pedestrians.
  • Keywords
    computer vision; inference mechanisms; intelligent transportation systems; pedestrians; probability; road safety; ADAS; advanced driver assistance systems; map-based hazard inference; pedestrian safety; probabilistic position prediction; trajectory analysis; trajectory prediction; vision based pedestrian trajectory tracking; Atmospheric measurements; Cameras; Hazards; Particle measurements; Roads; Tracking; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Vehicles Symposium (IV), 2015 IEEE
  • Conference_Location
    Seoul
  • Type

    conf

  • DOI
    10.1109/IVS.2015.7225707
  • Filename
    7225707