Based on instance-level temporal feature fusion and multi-stage association for 3D multi-object tracking

Aiming at the problems of insufficient utilisation of temporal information in the feature extraction process of existing multi-object tracking algorithms, as well as the problems of occluded trajectory localisation offset and trajectory loss during data association, we propose a 3D joint detection multi-object tracking algorithm based on instance-level temporal feature fusion and multi-stage association. The instance-level temporal feature fusion (ITFF) module utilizes attention mechanisms to merge instance-level features from previous and current frames, thereby alleviating the impact of object occlusion on the accuracy of trajectory regression. The trajectory correction (TC) module takes the trajectory regression box associated with the low confidence detection box as the output of the final trajectory to improve the precision of trajectory localisation. The trajectory recovery (TR) module further enhances the tracking performance of the model by fusing trajectory confidence to determine if a trajectory exists. The proposed algorithm is validated and tested on the KITTI dataset, achieving a high-order tracking accuracy (HOTA) of 76.01% on the test set, indicating better tracking performance and better robustness in occlusion scenarios.