Multi-Target Tracking Method Based on Improved Radar and Camera Data Association

The fusion of 4D millimeter-wave imaging radar and camera is an important development trend of advanced driver assistance systems and autonomous driving. In the field of multi-target tracking, the tracking is easy to lose due to the mutual occlusion of targets in the camera view. Therefore, combining the advantages of visual sensors and 4D millimeter-wave radar, a multi-sensor information fusion association algorithm is proposed. First, the 4D millimeter-wave radar point cloud is preprocessed, outliers are removed, and target-related information in the image is detected; then the point cloud is projected onto the image, and the targets in the segmented region are filtered. The filtered point cloud is clustered, and the correlation between the region projected onto the image and the detection box is calculated. Then use the unscented Kalman filter to predict, design rules to associate targets, and update innovation by multi-point weighting. This paper integrates the information of 4D millimeter-wave radar and camera well when the image target is occluded, thereby obtaining better target recognition and tracking.