Stereo based region of interest generation for pedestrian detection in driver assistance systems
Maral Mesmakhosroshahi, Joohee Kim, Yun‐Sik Lee, Jong-bok Kim · 2013
Pedestrian detection is one of the major goals in advanced driver assistance systems (ADAS) which has become an active research area in recent years. In this paper, we present a stereo based pedestrian detection system by fusing the depth and color data provided by a stereo vision camera on a moving platform. The proposed method uses an adaptive window for region of interest (ROI) generation using dense depth map. The extracted candidates are then applied to a Histogram of Oriented Gradients (HOG) feature descriptor to refine ROIs and Support Vector Machine (SVM) is used to classify them into pedestrian and non-pedestrian classes. The system is tested on a stereo based DAS dataset and results show that our system is able to detect pedestrians with different scales and illumination conditions and in presence of partial occlusion.