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

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

EVALUATION OF A COMPUTER VISION TRAFFIC SURVEILLANCE SYSTEM

This thesis presents an evaluation of the accuracy of a novel computer vision traffic sensor - developed by the Clemson University Electrical and Civil Engineering Departments - capable of collecting a variety of traffic parameters. More specific, the thesis examines how the camera height and distance from the travel way affects the accuracy. The details of the quantitative and qualitative evaluations used to validate the system are provided. The parameters chosen to evaluate were volume, vehicle classification, and speed. Experimental results of cameras mounted at heights of 20 and 30 feet and a lateral distance of 10 and 20 feet show accuracy as high as 98 percent for volume and 99 percent for vehicle classification. Results also showed discrepancies in speeds as low as 0.031 miles per hour. Some issues which affected the accuracy were shadows, occlusions, and double counting caused by coding detection errors