Multimodal Localization Distillation Method for 3D Single Object Tracking Task in Intelligent Transportation Systems

In 3D intelligent transportation systems, effectively using multimodal data from cameras and LiDAR for object tracking is a key research area. Current methods struggle due to an overreliance on 3D LiDAR data that overlooks detailed 2D camera imagery and inefficient use of localization data, resulting in poor frame-by-frame object tracking. Therefore, we propose a Multimodal Localization Fusion Tracking (MLFT) method to overcome these problems in intelligent transportation systems. The proposed method enables precise localization of multimodal information inputs through a novel Multimodal Localization Distillation (MMLD) approach and achieves accurate frame-to-frame object tracking via an advanced Multimodal Knowledge Transform-Fusion Tracking (MKTF) technique. The proposed method is evaluated using three representative datasets: KITTI, NuScenes, and Waymo Open. The experimental results show that our proposed MLFT algorithm has strong performance, improving the success rate by about 3.0%, 4.1%, and 3.3% on the KITTI, NuScenes, and Waymo Open datasets, respectively, while improving the accuracy by about 3.2%, 3.1%, and 4.1%.