We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition.
- Authors
Honghong Yang; Shiru Qu
- Abstract
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 'lowrank + 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.
- Subjects
TRAFFIC flow; TRAFFIC density; TRAFFIC incident management; ALGORITHMS; TRAFFIC engineering
- Publication
IET Intelligent Transport Systems (Wiley-Blackwell), 2018, Vol 12, Issue 1, p75
- ISSN
1751-956X
- Publication type
Article
- DOI
10.1049/iet-its.2017.0047