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dc.contributor.authorWu, Meiqingen_US
dc.contributor.authorZhou, Chengjuen_US
dc.contributor.authorSrikanthan, Thambipillaien_US
dc.identifier.citationWu, M., Zhou, C. & Srikanthan, T. (2016). Robust and low complexity obstacle detection and tracking. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1249-1254.
dc.description.abstractObstacle detection and tracking is essential module for autonomous driving. Vision based obstacle detection and tracking faces huge challenges due to factors like cluttered background, partial occlusion, inconsistent illumination, etc. In this paper, we propose a robust and low complexity stereovision based obstacle detection and tracking framework. Low complexity techniques are employed to detect obstacles in the u-v-disparity image space. In addition, effective strategies are proposed to construct a distinctive object appearance model for data association efficiently. Finally, an online multi-object tracking framework is proposed by integrating the obstacle detection and data association modules in a robust way. Extensive experimental results on the well-known KITTI tracking dataset demonstrate that the proposed method is able to detect and track various obstacles robustly and efficiently in diverse challenging scenarios.en_US
dc.rights© 2016 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved.en_US
dc.subjectEngineering::Computer science and engineering::Hardwareen_US
dc.titleRobust and low complexity obstacle detection and trackingen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conference2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)en_US
dc.contributor.researchHardware & Embedded Systems Lab (HESL)en_US
dc.subject.keywordsObstacle Detectionen_US
dc.subject.keywordsObstacle Trackingen_US
dc.citation.conferencelocationRio de Janeiro, Brazilen_US
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