Abstract:
We present a new vision-based pedestrian detection system for rear-view cameras which is robust to partial occlusions and non-upright poses. Detection is made using a sin...Show MoreMetadata
Abstract:
We present a new vision-based pedestrian detection system for rear-view cameras which is robust to partial occlusions and non-upright poses. Detection is made using a single automotive rear-view fisheye lens camera. The system uses “Accelerated Feature Synthesis”, a multiple-part based detection method with state-of-the-art performance. In addition, we collected and annotated an extensive dataset of videos for this specific application which includes pedestrians in a wide range of environmental conditions. Using this dataset we demonstrate the benefits of using part-based detection for detecting people in various poses and under occlusions. We also show, using a measure developed specifically for video-based evaluation, the gain in detection accuracy compared with template-based detection.
Published in: 2014 IEEE Intelligent Vehicles Symposium Proceedings
Date of Conference: 08-11 June 2014
Date Added to IEEE Xplore: 17 July 2014
Electronic ISBN:978-1-4799-3638-0
Print ISSN: 1931-0587