![]() Vehicles’ pixel coordinates are transformed to camera coordinates. In this step, several key points with particular transmission value for generating necessary calculation equations on road surface are selected to calibrate the camera. Finally, scene transmission image is got by dark channel prior theory, camera’s intrinsic and extrinsic parameters are calculated based on the parameter calibration formula deduced from monocular model and scene transmission image. Secondly, traffic road surface plane will be found by generating activity map created by calculating the expected value of the absolute intensity difference between two adjacent frames. If it is homogenous fog, average pixel value from top to bottom in the selected area will change in the form of edge spread function (ESF). Firstly, current video frame is recognized to discriminate current weather condition based on area search method (ASM). Three major steps are included in our algorithm. Once calibrated, scene distance will be got and can be used to calculate vehicles average velocity. Painted lines in scene image are neglected because sometimes there are no traffic lanes, especially in un-structured traffic scene. Unlike other researches in velocity calculation area, our traffic model only includes road plane and vehicles in motion. Camera fixed in the middle of the road should be calibrated in homogenous fog weather condition, and can be used in any weather condition. A novel algorithm for vehicle average velocity detection through automatic and dynamic camera calibration based on dark channel in homogenous fog weather condition is presented in this paper.
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