Feasibility study of a video-based urban traffic tracking system

This paper presents a feasibility study on the problem of tracking vehicles using multiple video cameras placed at known locations. Each video processor extracts time of arrival at the camera, instantaneous velocity, and image features such as color and size. The information is sent to a centralized tracker that estimates the location of the vehicles at each instant of time. Our study showed that our association algorithms across multiple camera views could achieve an 80% detection rate. We discovered that changes in resolution, view angle, elevation, and sun angle did not significantly affect our algorithm's performance. Our study also showed that with multiple hypothesis tracking and with adequate camera to camera image matching performance, tracks can be maintained for 10 km with 50% probability for 1600 meter camera spacing and 90% probability for 400 meter camera spacing. The conclusion is that current tracking technology with sufficient video matching performance is likely to give good tracking performance in an urban environment.