Vision-based road-traffic monitoring sensor

Current techniques for road-traffic monitoring rely on sensors that have limited capabilities and are often both costly and disruptive to install. The use of video cameras (many of which are already installed to survey road networks), coupled with computer vision techniques, offers an attractive alternative to current sensors. Vision-based sensors have the potential to measure a greater variety of traffic parameters (e.g. entry/exit statistics, journey times and incident detection) while installation and maintenance may be performed without disruption to traffic flow. Work on a model based approach for locating vehicles in images of complex road scenes is presented. The location of the vehicle in the image is transformed to the vehicle's position and orientation in the real world while the deformable vehicle model allows the vehicle's principal dimensions to be measured. This data may be passed to a high level tracking algorithm to extract traffic parameters such as vehicle speed vehicle count, and junction entry/exit statistics. The principal dimensions may be used to classify the vehicle within categories such as car, van or bus. The system could also be used as a boot-strap process for faster, but perhaps less robust, tracking algorithms. The key features of the system are described and results from testing it on images from real traffic scenes are presented.