Pedestrian detection, which has wide applications on surveillance, automatic driving and robotics, plays a significant role in computer vision... Show morePedestrian detection, which has wide applications on surveillance, automatic driving and robotics, plays a significant role in computer vision. Among all kinds of pedestrian detection methods, stereo based method achieves an accurate and efficient detection result by exploiting depth and color information. However, many stereo based systems fail at considering motion information which is important in locating and detecting an object. For many pedestrian detection systems, adding extra data like motion is one of the most effective ways to improve the performance. Therefore, this thesis proposes a multi-cue pedestrian detection system which integrates optical flow based and stereo based modules for combining motion, depth and color information. In the proposed system, optical flow and disparity value are estimated by using the frames which obtained from a stereo camera. In order to obtain accurate pedestrian motion, ego motion is compensated by using motion clustering, affine model and RANSAC. After that, the motion and the depth information are exploited for ROI generation. Finally, SVM is trained by the combination of motion feature and HOG feature. Experimental results show that the use of high-accuracy optical flow along with depth and color information improves the performance of multi-cue pedestrian detection system. M.S. in Electrical Engineering, December 2015 Show less
