Improved Accuracy of Track-Based Integration of Radar and Vision Sensors

This article proposes a method for integrating the camera and radar data using nonlinear homography transformations. The lens distortion effects of a camera cause differences in object positions to have a nonlinear relationship with pixel differences in the captured image. Therefore, accurate sensor integration is impossible when using linear homography transformations to represent camera track information on the radar coordinate plane or vice versa. Thus, this article proposes a method to integrate the camera-estimated track with the radar track by applying lateral and longitudinal corrections. For lateral corrections, the reference point of the camera track is adjusted from the bottom center of the bounding box to a position that reflects the angle between the vanishing point and the target. For longitudinal corrections, a nonlinear homography transformation is used to address the accuracy degradation caused by lens distortion. Using experimental data, we demonstrate that track matching accuracy significantly improved from an initial root mean square error of 0.92–0.45 m after applying two separate correction steps in each direction, highlighting the effectiveness of the correction process in enhancing sensor accuracy.