access icon free Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition

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.

Inspec keywords: video signal processing; Kalman filters; traffic engineering computing; target tracking; image sequences; object detection; real-time systems

Other keywords: online Kalman filter algorithm; background subtraction model; video sequences; qualitative evaluations; traffic control; complex traffic scenes; moving vehicle detection; traffic management; multiple moving objects; quantitative evaluations; real-time traffic information; effective vehicle counting system; low-rank decomposition; vehicle tracking; detection performance evaluation

Subjects: Filtering methods in signal processing; Traffic engineering computing; Computer vision and image processing techniques; Video signal processing; Optical, image and video signal processing

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