Journal
IET INTELLIGENT TRANSPORT SYSTEMS
Volume 12, Issue 1, Pages 75-85Publisher
WILEY
DOI: 10.1049/iet-its.2017.0047
Keywords
real-time systems; traffic engineering computing; object detection; target tracking; Kalman filters; image sequences; video signal processing; complex traffic scenes; background subtraction model; low-rank decomposition; traffic control; traffic management; real-time traffic information; effective vehicle counting system; vehicle tracking; moving vehicle detection; online Kalman filter algorithm; multiple moving objects; video sequences; detection performance evaluation; qualitative evaluations; quantitative evaluations
Funding
- China Astronautic Science and Technology Innovation Foundation [CASC201104]
- China Aviation Science Fund Project [2012ZC53043]
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Real-time vehicle counting can efficiently improve traffic control and management. Aiming to efficiently collect the real-time traffic information, the authors propose an effective vehicle counting system for detecting and tracking vehicles in complex traffic scenes. The proposed algorithm detects moving vehicles based on background subtraction method with low-rank+sparse' decomposition. For accurately counting vehicles, an online Kalman filter algorithm is used to track the multiple moving objects and avoid counting one vehicle repeatedly. The proposed method is evaluated on three publicly available datasets, which include seven video sequences with various challenging scenes for detection performance evaluation, and another two video sequences for vehicle counting evaluation. The experimental results demonstrate a good performance of the proposed method in terms of both qualitative and quantitative evaluations.
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