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The intelligent transportation system (ITS) is inseparable from people’s lives, and the development of artificial intelligence has made intelligent video surveillance systems more widely used. In practical traffic scenarios, the detection and tracking of vehicle targets is an important core aspect of intelligent surveillance systems and has become...
Citations
... Then, both [7,8] addressed the calibration problem using optimization methods. Precise installation of sensors was achieved in [10][11][12] for radar and camera spatial synchronization. Fu et al [10] and Xu et al [12] measured the installation height and angle of sensors, while Wang et al [11] gauged the pose of each sensor relative to the road plane. ...
... Precise installation of sensors was achieved in [10][11][12] for radar and camera spatial synchronization. Fu et al [10] and Xu et al [12] measured the installation height and angle of sensors, while Wang et al [11] gauged the pose of each sensor relative to the road plane. On this basis, [12] proposed an improved Hausdorff distance matching algorithm to correlate and fuse radar and camera measurements. ...
... Fu et al [10] and Xu et al [12] measured the installation height and angle of sensors, while Wang et al [11] gauged the pose of each sensor relative to the road plane. On this basis, [12] proposed an improved Hausdorff distance matching algorithm to correlate and fuse radar and camera measurements. However, limited by the accuracy of manual measurements, manual calibration methods are generally inaccurate. ...
Accurate perception of the movement and appearance of vehicles depends on the robustness and reliability of the extrinsic parameters calibration in a multi-sensor fusion scenario. However, conventional calibration methods require manual acquisition of prior information, leading to high labor costs and low calibration accuracy. Therefore, we proposed an automatic coarse-to-fine calibration method for roadside radar and camera sensors to lower costs and improve accuracy. Next, an association strategy based on fluctuating traffic volumes was also developed to assist in robust target matching during the coarse-to-fine calibration process. Finally, extrinsic parameters between the Radar Coordinate System (RCS) and Camera Coordinate System (CCS) were calibrated through double rotations of the position vectors (PV) obtained from each system. To verify the proposed method, an experiment was conducted on a pedestrian bridge using an uncalibrated 4D millimeter-wave radar and a traffic monocular camera. The results showed that our proposed method reduced the interquartile range of the roll angle by 41.5% compared to a state-of-the-art neural network method. It also outperformed the manual calibration method by 2.47% in terms of the average reprojection error.
