Keypoint Trajectory Estimation Using Propagation Based Tracking
2016 (English)In: 2016 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), IEEE , 2016, 933-939 p.Conference paper (Refereed)
One of the major steps in visual environment perception for automotive applications is to track keypoints and to subsequently estimate egomotion and environment structure from the trajectories of these keypoints. This paper presents a propagation based tracking method to obtain the 2D trajectories of keypoints from a sequence of images in a monocular camera setup. Instead of relying on the classical RANSAC to obtain accurate keypoint correspondences, we steer the search for keypoint matches by means of propagating the estimated 3D position of the keypoint into the next frame and verifying the photometric consistency. In this process, we continuously predict, estimate and refine the frame-to-frame relative pose which induces the epipolar relation. Experiments on the KITTI dataset as well as on the synthetic COnGRATS dataset show promising results on the estimated courses and accurate keypoint trajectories.
Place, publisher, year, edition, pages
IEEE , 2016. 933-939 p.
IEEE Intelligent Vehicles Symposium, ISSN 1931-0587
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-134102DOI: 10.1109/IVS.2016.7535500ISI: 000390845600148ISBN: 978-1-5090-1821-5 (print)OAI: oai:DiVA.org:liu-134102DiVA: diva2:1067522
IEEE Intelligent Vehicles Symposium (IV)