Embedded vehicle speed estimation system using an asynchronous temporal contrast vision sensor
EURASIP Journal on Embedded Systems
Vehicle speed detection from a single motion blurred image
Image and Vision Computing
Ground plane velocity estimation embedding rectification on a particle filter multi-target tracking
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Estimating traffic intensity using profile images on rectified images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Dynamic data regulation for fixed vehicle detectors
IEEE Transactions on Intelligent Transportation Systems
A channel awareness vehicle detector
IEEE Transactions on Intelligent Transportation Systems
A taxonomy and analysis of camera calibration methods for traffic monitoring applications
IEEE Transactions on Intelligent Transportation Systems
Roadside camera calibration and its application in length-based vehicle classification
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Journal of Signal Processing Systems
Velocity calculation by automatic camera calibration based on homogenous fog weather condition
International Journal of Automation and Computing
Traffic meteorological visibility estimation based on homogenous area extraction
International Journal of Computer Applications in Technology
Hierarchical camera auto-calibration for traffic surveillance systems
Expert Systems with Applications: An International Journal
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In this paper, we present a new three-stage algorithm to calibrate roadside traffic management cameras and track vehicles to create a traffic speed sensor. The algorithm first estimates the camera position relative to the roadway using the motion and edges of the vehicles. Given the camera position, the algorithm then calibrates the camera by estimating the lane boundaries and the vanishing point of the lines along the roadway. The algorithm transforms the image coordinates from the vehicle tracker into real-world coordinates using our simplified camera model. We present results that demonstrate the ability of our algorithm to produce good estimates of the mean vehicle speed in a lane of traffic.