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.
Real‐time vehicle detection and counting in complex traffic scenes using background subtraction model with low‐rank decomposition
IET Intelligent Transport Systems ; 12 , 1 ; 75-85
2018-02-01
11 pages
Article (Journal)
Electronic Resource
English
traffic control , qualitative evaluations , Kalman filters , effective vehicle counting system , moving vehicle detection , video sequences , image sequences , detection performance evaluation , object detection , real‐time traffic information , real‐time systems , complex traffic scenes , multiple moving objects , traffic engineering computing , video signal processing , online Kalman filter algorithm , vehicle tracking , target tracking , background subtraction model , quantitative evaluations , traffic management , low‐rank decomposition
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