Highway Camera Calibration and Vehicle Speed Estimation Using Multilayered Lane-Line Keypoints

Camera calibration enables the automatic estimation of intrinsic and extrinsic camera parameters, uncovering correspondences between 2D images and 3D real-world coordinates. For highway surveillance cameras, existing methods often rely on cumbersome procedures to extract limited priors (e.g., vanishing points or reference points) and provide incomplete estimations (e.g., roll angle). Therefore, we leverage the multilayered lane lines on highways, which offer rich priors such as segment lengths, intervals, and lane widths, to develop a novel camera calibration and vehicle speed estimation method. For camera calibration, our approach performs road instance segmentation and extractsmultilayered lane-line keypoints (MLK)while mitigating environmental interference and dynamic vehicle occlusions. An MLK-based calibration model is constructed and anangle-polling Levenberg-Marquardt algorithmis designed to estimate key parameters, including focal length, three rotation angles, and lane-line distance. For vehicle speed estimation, multi-object tracking (MOT) algorithms are integrated with the calibration model to infer the average speeds of all identified vehicles. We collected real highway video footage from four different camera setups in Chinese highways. Experimental results demonstrate that our method outperforms existing methods across all setups. The impact of key parameters is evaluated to determine the optimal configuration. Lastly, its effectiveness in vehicle speed estimation is assessed based on advanced MOT algorithms.