3D multi-object tracking is an important component of autonomous driving technology. Recent 3D multi-object tracking methods still suffer from issues such as information loss during the fusion of multimodal features, weak discriminative power of the association matrix, and poor robustness of single similarity measure. To address these problems, this paper proposes a Multimodal Collaboration-guided Cascade Association network for 3D multi-object tracking (MCCA-MOT). We design a point cloud feature adaptive diffusion fusion module. This module utilizes inverse distance weighting aggregation diffusion technology to address the issue of information loss during the feature fusion process. This enhances the tracking performance of small objects. Secondly, we propose a dynamic sampling feature cooperative fusion module. This module performs fine-grained local-global feature cooperative fusion based on dynamic sampling, enhancing the distinctiveness of object features. It improves the tracking capability of occluded objects. Finally, in the multi-similarity measure-driven cascading association module, we construct a more discriminative association matrix using multiple types of information and design a cascading strategy. This strategy applies different similarity measures at different association stages for objects with ambiguous features. This reduces identity switches and trajectory fragmentation. Extensive experiments on the KITTI dataset demonstrate the superiority of our method in various performance metrics. Our detailed implementations can be obtained at https://github.com/yuanfuture/MCCA-MOT.
MCCA-MOT: Multimodal Collaboration-Guided Cascade Association Network for 3D Multi-Object Tracking
IEEE Transactions on Intelligent Transportation Systems ; 26 , 1 ; 974-989
2025-01-01
4554333 byte
Article (Journal)
Electronic Resource
English
FANTRACK: 3D MULTI-OBJECT TRACKING WITH FEATURE ASSOCIATION NETWORK
British Library Conference Proceedings | 2019
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