Trajectory Analysis and Prediction for Improved Pedestrian Safety: Integrated Framework and Evaluations

Publication: Research - peer-reviewArticle in proceeding

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.
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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.
Original languageEnglish
Title of host publicationIEEE Intelligent Vehicles Symposium (IV), 2015
Number of pages6
PublisherIEEE
Publication date2015
Pages330-335
ISBN (print)978-1-4673-7266-4
DOI
StatePublished - 2015
Event - Seoul, Korea, Republic of

Conference

Conference2015 IEEE Intelligent Vehicles Symposium
LocationCOEX
LandKorea, Republic of
BySeoul
Periode28/06/201501/07/2015

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