Intelligent traffic management system for cross section of roads using computer vision
The increasing proliferation of traffic monitoring technology has brought about sophisticated techniques for traffic monitoring such as motion tracking using active or optical sensors. Image processing techniques to identify vehicles and track velocity are possible using real time video feedback from traffic cameras along major roads and highways. However, many cities have limitations on camera and equipment quality which obstruct traffic monitoring processes. In Honolulu, the traffic images posted on the traffic monitoring website have a 3 minutes delay between frames. This makes it impossible to perform vehicle tracking based on those images. Variations in camera angles and low spatial resolution also make the task of monitoring traffic more difficult. In this paper two simple traffic density estimators with two different background models are implemented and compared to each other. The estimator first separates traffic foreground from road background using moving average or codebook methods. A modified Hough transformation identifies potential road area and then the traffic density is quantified as percentage of traffic contained within the road area of an image. These techniques deal with the limitations of traffic images with low spatial resolution and low frame rate.
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