Can Vehicle Become a New Pattern for Roadside Camera Calibration?

Abstract

Roadside camera calibration is essential to intelligent traffic surveillance and still an unsolved problem. The commonly used pattern-based calibration methods are suitable for the laboratory environment rather than real traffic environment, since the calibration patterns (e.g., checkerboards) generally do not exist in traffic scenarios. In view of this, we propose a new framework for roadside camera calibration where the vehicle moving on the roadway is first introduced as a calibration pattern. Considering that the vehicles are main monitoring targets and inevitably appear in traffic scenarios, the proposed calibration method has a wide use range and is not limited to the structure information of traffic scenarios. Inspired by the traditional pattern-based calibration methods that utilize the matching of 3D-2D point correspondences, we utilize the 3D-2D vehicle matching for camera calibration. The key insight is to convert the camera calibration problem into a vehicle matching problem. To improve the accuracy of calibration results, a new measure function is provided to evaluate the vehicle matching degree and a dynamic calibration method using multi-frame information is proposed to correct camera parameters. Experiments on real traffic images demonstrate the effectiveness and practicability of the proposed calibration framework.