OptInOpt: Dual Optimization for Automatic Camera Calibration by Multi-Target Observations

In this paper, we propose a new approach to automatic calibration of surveillance cameras. The proposed method is based on observing rigid objects in the scene and automatically estimating landmarks on these objects. The proposed approach can use arbitrary rigid objects, as was verified by experiments with a synthetic dataset, but vehicles were used during our experiments with real-life data. Landmarks on objects automatically detected by a convolutional neural network together with corresponding 3D positions in the object coordinate system are exploited during the camera calibration process. To determine 3D positions of the landmarks, fine-grained classification of the detected vehicles in the image plane is necessary. The proposed calibration method consists of dual optimization-optimization of objects' positions in the world coordinate system and also optimization of the calibration parameters to minimize the re-projection error of the localized landmarks. The experiments show improvement in calibration accuracy over the existing method solving a similar problem furthermore with fewer restrictions on the input data. The calibration error on a real world dataset decreased from 6.88% to 2.85%.