Object Detection and Tracking for Autonomous Navigation in Dynamic Environments

Abstract We address the problem of vision-based navigation in busy inner-city locations, using a stereo rig mounted on a mobile platform. In this scenario semantic information becomes important: rather than modelling moving objects as arbitrary obstacles, they should be categorised and tracked in order to predict their future behaviour. To this end, we combine classical geometric world mapping with object category detection and tracking. Object-category specific detectors serve to find instances of the most important object classes (in our case pedestrians and cars). Based on these detections, multi-object tracking recovers the objects' trajectories, thereby making it possible to predict their future locations, and to employ dynamic path planning. The approach is evaluated on challenging, realistic video sequences recorded at busy inner-city locations. Keywords object category detection

• Type: Journal
• Year: 2010
• Where: IJRR
• Authors: Andreas Ess, Konrad Schindler, Bastian Leibe, Luc J. Van Gool
• Keywords: Busy Inner-city Locations, Category Detection, Dynamic Path, IJRR 2010