3D Vehicle Extraction and Tracking from Multiple Viewpoints for Traffic Monitoring by using Probability Fusion Map

• Main Authors: Zhencheng Hu, Chenhao Wang, Keiichi Uchimura
• Format: Article
• Language: English
• Published: Computer Vision Center Press 2008-02-01
• Series: ELCVIA Electronic Letters on Computer Vision and Image Analysis
• Subjects: Probability Fusion Map; 3D Modeling; Multiple View; Traffic Monitoring
• Online Access: https://elcvia.cvc.uab.es/article/view/178

This paper presents a novel solution of vehicle occlusion and 3D measurement for traffic monitoring by data fusion from multiple stationary cameras. Comparing with single camera based conventional methods in traffic monitoring, our approach fuses video data from different viewpoints into a common probability fusion map (PFM) and extracts targets. The proposed PFM concept is efficient to handle and fuse data in order to estimate the probability of vehicle appearance, which is verified to be more reliable than single camera solution by real outdoor experiments. An AMF based shadowing modeling algorithm is also proposed in this paper in order to remove shadows on the road area and extract the proper vehicle regions.