Multi-object Tracking Based on a Novel Feature Image with Multi-modal Information

Multi-object tracking technology plays a crucial role in many applications, such as autonomous vehicles and security monitoring. This paper proposes a multi-object tracking framework based on the multi-modal information of 3D point clouds and color images. At each sampling instant, the 3D point cloud and image acquired by a LiDAR and a camera are fused into a color point cloud, where objects are detected by the Point-GNN method. And, a novel height-intensity-density (HID) image is constructed from the bird's eye view. The HID image truly reflects the shapes and materials of objects and effectively avoids the influence of object occlusion, which is helpful to object tracking. In two sequential HID images, a new rotation kernel correlation filter is proposed to predict the objects. Furthermore, an object retention module and an object re-recognition module are developed to overcome the object matching failure in the in-between frames. The proposed method takes full advantage of the multi-modal data and effectively achieves the information complementation to improve the accuracy of multi-object tracking. The experiments with the KITTI dataset show that the proposed method has the best performance among the existing traditional multi-object tracking methods.