Self-Calibration of Traffic Surveillance Camera using Motion Tracking

A statistical and computer vision approach using tracked moving vehicle shapes for auto-calibrating traffic surveillance cameras is presented. Vanishing point of the traffic direction is picked up from linear regression of all tracked vehicle points. Preliminary straightening model is then built to help collect statistics of the typical vehicle class traveling in each particular scene. Analysis on this class eventually helps to compute the complete calibration parameters. Results obtained from the validation step against traditional methods in different traffic locations demonstrate its desirable accuracy with much more flexibility and reliability.