Moving Object Detection in Real-Time Using Stereo from a Mobile Platform

Abstract

This paper presents a mobile object detection algorithm which performs with two consecutive stereo images. Like most motion detection methods, the proposed one is based on dense stereo matching and optical flow (OF) estimation. Noting that the main computational cost of existing methods is related to the estimation of OF, we propose to use a fast algorithm based on Lucas–Kanade paradigm. We then derive a comprehensive uncertainty model by taking into account all the estimation errors occurring during the process. In contrast with most previous works, we rigorously expand the error related to vision based ego-motion estimation. Finally, we present a comparative study of performance on the challenging KITTI dataset which demonstrates the effectiveness of the proposed approach.