Predict Vehicle Collision by TTC From Motion Using a Single Video Camera
The objective of this paper is the instantaneous computation of time-to-collision (TTC) for potential collision only from the motion information captured with a vehicle borne camera. The contribution is the detection of dangerous events and degree directly from motion divergence in the driving video, which is also a clue used by human drivers. Both horizontal and vertical motion divergence are analyzed simultaneously in several collision sensitive zones. The video data are condensed to the motion profiles both horizontally and vertically in the lower half of the video to show motion trajectories directly as edge traces. Stable motion traces of linear feature components are obtained through filtering in the motion profiles. As a result, this avoids object recognition and sophisticated depth sensing in prior. The fine velocity computation yields reasonable TTC accuracy so that a video camera can achieve collision avoidance alone from the size changes of visual patterns. The authors have tested the algorithm for various roads, environments, and traffic, and shown results by visualization in the motion profiles for overall evaluation.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2019, IEEE.
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Authors:
- Kilicarslan, M
- Zheng, Jiang Yu
- Publication Date: 2019-2
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 522-533
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 20
- Issue Number: 2
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Computer vision; Crash avoidance systems; Data analysis; Driver support systems; In vehicle sensors; Machine learning; Near crashes; Video; Warning systems
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01696775
- Record Type: Publication
- Files: TLIB, TRIS
- Created Date: Mar 1 2019 9:24AM