Dense Stereo-Based ROI Generation for Pedestrian Detection

This paper investigates the benefit of dense stereo for the ROI generation stage of a pedestrian detection system. Dense disparity maps allow an accurate estimation of the camera height, pitch angle and vertical road profile, which in turn enables a more precise specification of the areas on the ground where pedestrians are to be expected. An experimental comparison between sparse and dense stereo approaches is carried out on image data captured in complex urban environments (i.e. undulating roads, speed bumps). The ROI generation stage, based on dense stereo and specific camera and road parameter estimation, results in a detection performance improvement of factor five over the stateof-the-art based on ROI generation by sparse stereo. Interestingly, the added processing cost of computing dense disparity maps is at least partially amortized by the fewer ROIs that need to be processed at the system level.