Abstract
Traffic analyses, particularly speed measurements, are highly valuable in terms of road safety and traffic management. In this paper, an analytical model is presented to measure the speed of a moving vehicle using an off-the-shelf video camera. The method utilizes the temporal sampling rate of the camera and several intrusion lines in order to estimate the probability density function (PDF) of a vehicle’s speed. The proposed model provides not only an accurate estimate of the speed, but also the possibility of being able to study the performance boundaries with respect to the camera frame rate as well as the placement and number of intrusion lines in advance. This analytical model is verified by comparing its PDF outputs with the results obtained via a simulation of the corresponding movements. In addition, as a proof-of-concept, the proposed model is implemented for a video-based vehicle speed measurement system. The experimental results demonstrate the model’s capability in terms of taking accurate measurements of the speed via a consideration of the temporal sampling rate and lowering the deviation by utilizing more intrusion lines. The analytical model is highly versatile and can be used as the core of various video-based speed measurement systems in transportation and surveillance applications.
Highlights
Traffic surveillance systems collect and analyze road transportation data in order to improve road flow and safety
An analytical model is introduced for video-based speed measurement that is based on intrusion lines, handheld computers, and an integrated camera
The proposed model considers the temporal sampling of the camera, which affects the uncertainty of the measurements
Summary
Traffic surveillance systems collect and analyze road transportation data in order to improve road flow and safety. Since vehicles constitute the main component in road transportation, it is necessary to measure their respective parameters such as flow, speed, direction, and density. The common configuration is to have a camera facing down at the road alongside a lane to capture video frames of vehicles. Consecutive frames are processed using computer vision techniques in order to calculate the vehicle’s speed. The vehicle’s location is extracted from the background in consecutive frames. In [6], a background model of the road is created and the camera vibration is compensated to reduce the noise and improve the measurement. In [7], a solar-powered automated speed violation detection system is presented
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