Automatic Calibration for Monocular Cameras in Highway Scenes via Vehicle Vanishing Point Detection

Automatic camera calibration is a fundamental technology for 3D traffic parameter extraction. With the popularity of pan-tilt-zoom cameras, this technique demonstrates great potential to enhance traffic safety and efficiency, especially for highways. This paper aims to present a fully automatic calibration method for surveillance cameras in highway scenes. Our system is divided into two stages. In the first stage, a deep convolution neural network was used to estimate a pair of orthogonal vanishing points from multiple vehicles. This process transformed vanishing point detection into an estimation of vehicle direction, which was further determined by introducing the central residual mechanism. In the diamond space, the straight lines formed by these directions accumulated the final positions of the vanishing points. More importantly, we proposed a novel algorithm for estimating the lane width using vehicle trajectories in the second stage. It can be used to calculate the camera height, making the calibration fully automated. We also corrected the distorted lens using vehicle trajectories. Comprehensive experiments were conducted on the proposed data set and the BoxCars116k data set. The results indicate that the composite mechanism (i.e., classification and central residual) significantly improves the accuracy and robustness of the vanishing point estimation. Combined with automatic camera height estimation, our technology is superior to the most representative methods in calibration performance. Since it does not have any constraints on road geometry and camera placement, our approach applies to most highway surveillance systems.