An Efficient Approach for Dynamic Calibration of Multiple Cameras

In this paper, we propose a new algorithm for dynamic calibration of multiple cameras. Based on the mapping between a horizontal plane in the 3-D space and the 2-D image plane on a panned and tilted camera, we utilize the displacement of feature points and the epipolar-plane constraint among multiple cameras to infer the changes of pan and tilt angles for each camera. This algorithm does not require a complicated correspondence of feature points. It can be applied to surveillance systems with wide-range coverage. It also allows the presence of moving objects in the captured scenes while performing dynamic calibration. The sensitivity analysis of our algorithm with respect to measurement errors and fluctuations in previous estimations is also discussed. The efficiency and feasibility of this approach has been demonstrated in some experiments over real scenery. Note to Practitioners-For a surveillance system with multiple cameras, the poses of cameras may be changed from time to time to acquire different views of the monitored scene. Whenever the poses of cameras are changed, the relative positioning and orientation among cameras need to be recalibrated. In this paper, we demonstrate a new and efficient approach to calibrate multiple cameras dynamically. The concept of our approach originated from the observation that people usually can identify the directions of the pan and tilt angles, and even make a rough estimate about the changes of pan and tilt angles, simply based on some clues revealed in the captured images. In our approach, a set of cameras are first calibrated based on a static calibration method. As cameras begin to pan or tilt, the images of these cameras change accordingly. We keep extracting and tracking a few feature points from these images. Based on the displacement of these feature points in consecutive images, the pan and tilt angle changes of the cameras can be automatically estimated via the proposed approach. There is no need to place calibration patterns or landmarks while performing dynamic calibration. In addition, there is no need to perform the complicated correspondence of feature points among cameras. The proposed approach is practical for a wide-range surveillance system with multiple cameras and is applicable for complicated environments. In the future, we will combine the proposed approach with an object tracking system to develop an efficient active surveillance system with multiple cameras.