Bicyclist detection in large scale naturalistic driving video

Monocular based bicyclist detection in naturalistic driving video is a very challenging problem due to the high variance of the bicyclist appearance and complex background of naturalistic driving environment. In this paper, we propose a two-stage multi-modal bicyclist detection scheme to efficiently detect bicyclists with varied poses for further behavior analysis. A new motion based region of interest (ROI) detection is first applied to the entire video to refine the region for sliding-window detection. Then an efficient integral feature based detector is applied to quickly filter out the negative windows. Finally, the remaining candidate windows are encoded and tested by three pre-learned pose-specific detectors. The experimental results on our TASI 110 car naturalistic driving dataset show the effectiveness and efficiency of the proposed method. The proposed method outperforms the traditional.