3D vehicle model-based PTZ camera auto-calibration for smart global village

Abstract

PTZ cameras perform a critical role in highway surveillance systems because of their wide field of vision and high degree of flexibility. However, it is difficult to obtain accurate real-world physical information by using the acquired image information due to changes in focal length and angles of the PTZ cameras. Therefore, it is of great value to investigate off-site auto-calibration methods for PTZ cameras in highway surveillance system applications. This paper proposes a PTZ camera auto-calibration method based on vanishing points and 3D vehicle model constraints to establish an accurate description of the relationship between image information and real-world physical information. Firstly, the position of the longitudinal vanishing point is estimated using the cascaded Hough transform voting of the vehicle target motion trajectories. Secondly, the physical size of the 3D vehicle model is used as a constraint, and it is combined with an enumeration strategy to obtain accurate estimation of the horizontal vanishing point. Finally, the calibration parameters of the PTZ camera are calculated with the known camera height. Experiments on numerous sets of highway surveillance scenes demonstrate that the proposed PTZ camera auto-calibration method is able to satisfy the application requirements of highway surveillance scenes. Compared with reference methods, this method exhibits superior performance. Moreover, this method possesses substantial value for various applications.