Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147485
Title: Robust and low complexity obstacle detection and tracking
Authors: Wu, Meiqing
Zhou, Chengju
Srikanthan, Thambipillai
Keywords: Engineering::Computer science and engineering::Hardware
Issue Date: 2016
Source: Wu, 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. https://dx.doi.org/10.1109/ITSC.2016.7795717
Abstract: Obstacle 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.
URI: https://hdl.handle.net/10356/147485
ISBN: 9781509018895
DOI: 10.1109/ITSC.2016.7795717
Rights: © 2016 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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