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
2017-12-06
11 pages
Article (Journal)
Electronic Resource
English
object detection , online Kalman filter algorithm , video signal processing , vehicle tracking , background subtraction model , real-time traffic information , traffic management , Kalman filters , effective vehicle counting system , moving vehicle detection , quantitative evaluations , target tracking , qualitative evaluations , complex traffic scenes , low-rank decomposition , real-time systems , traffic engineering computing , video sequences , traffic control , multiple moving objects , image sequences , detection performance evaluation
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