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

Zhencheng Hu, Chenhao Wang, Keiichi Uchimura


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.


Probability Fusion Map; 3D Modeling; Multiple View; Traffic Monitoring

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