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Proceedings Paper

Vision-based multiple vehicle detection and tracking at nighttime
Author(s): Wencong Xu; Hai Liu
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Paper Abstract

In this paper, we develop a robust vision-based approach for real-time traffic data collection at nighttime. The proposed algorithm detects and tracks vehicles through detection and location of vehicle headlights. First, we extract headlights candidates by an adaptive image segmentation algorithm. Then we group headlights candidates that belong to the same vehicle by spatial clustering and generate vehicle hypotheses by rule-based reasoning. The potential vehicles are then tracked over frames by region search and pattern analysis methods. The spatial and temporal continuity extracted from tracking process is used to confirm vehicle's presence. To handle problem of occlusions, we apply Kalman Filter to motion estimation. We test the algorithm on the video clips of nighttime traffic under different conditions. The experimental results show that real-time vehicle counting and tacking for multi-lanes are achieved and the total detection rate is above 96%.

Paper Details

Date Published: 18 August 2011
PDF: 9 pages
Proc. SPIE 8194, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, 81941B (18 August 2011); doi: 10.1117/12.900131
Show Author Affiliations
Wencong Xu, Shandong Univ. At Weihai (China)
Hai Liu, Shandong Univ. At Weihai (China)


Published in SPIE Proceedings Vol. 8194:
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications
Makoto Ikeda; Nanjian Wu; Guangjun Zhang; Kecong Ai, Editor(s)

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