Many query-based approaches for 3D Multi-Object Tracking (MOT) adopt the
tracking-by-attention paradigm, utilizing track queries for identity-consistent
detection and object queries for identity-agnostic track spawning.
Tracking-by-attention, however, entangles detection and tracking queries in one
embedding for both the detection and tracking task, which is sub-optimal. Other
approaches resemble the tracking-by-detection paradigm, detecting objects using
decoupled track and detection queries followed by a subsequent association.
These methods, however, do not leverage synergies between the detection and
association task. Combining the strengths of both paradigms, we introduce
ADA-Track, a novel end-to-end framework for 3D MOT from multi-view cameras. We
introduce a learnable data association module based on edge-augmented
cross-attention, leveraging appearance and geometric features. Furthermore, we
integrate this association module into the decoder layer of a DETR-based 3D
detector, enabling simultaneous DETR-like query-to-image cross-attention for
detection and query-to-query cross-attention for data association. By stacking
these decoder layers, queries are refined for the detection and association
task alternately, effectively harnessing the task dependencies. We evaluate our
method on the nuScenes dataset and demonstrate the advantage of our approach
compared to the two previous paradigms. Code is available at
this https URL

一个名为 ADA-Track 的新颖框架对多视角相机的 3D 多目标跟踪进行端到端的研究，利用可学习的数据关联模块和基于边缘增强的交叉注意力，同时结合检测和数据关联任务，以实现更好的效果。