Polycepta不用死板的外观模板，边跟踪边更新目标长相，在KITTI基准上拿92.27% MOTA还跑得飞快，比传统方法稳多了。
Polycepta提出对象中心外观状态估计框架，将外观建模从帧级匹配转为递归估计，为每个跟踪目标独立维护并更新外观状态。在KITTI、Waymo Open Dataset和MOT17三个基准上，该方法一致减少身份切换并提升跟踪性能。集成到RobMOT框架后，Polycepta在KITTI上达到MOTA 92.27%，运行速度90.57 Hz。
Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking
The tracking-by-detection paradigm in multi-object tracking (MOT) typically relies on static appearance descriptors to complement motion estimation. However, these descriptors are frame-independent, limiting their robustness as visual cues. Since such descriptors are often obtained from computationally intensive pretrained backbones, real-time MOT systems frequently abandon appearance cues altogether and rely solely on motion prediction and geometric association. In this work, we introduce Polycepta, an object-centric appearance state estimation framework that reformulates appearance modeling as a recursive estimation problem rather than a frame-wise matching task. Polycepta constructs and continuously updates an independent appearance state for each tracked object, enabling future appearance representations to be estimated from accumulated observations. Polycepta is encouraged to learn the appearance-state construction of object-specific representations rather than memorize them through a proposed learning strategy, enabling appearance estimation for unseen classes. A key property of Polycepta is that the quality of appearance estimation improves as object states evolve during inference. While conventional appearance descriptors remain static or degrade over time, Polycepta progressively refines appearance estimates as additional observations are accumulated. Extensive experiments on KITTI, the Waymo Open Dataset, and MOT17 demonstrate consistent reductions in identity switches and improvements in tracking performance when integrated into the tracking-by-detection pipelines. Polycepta operates at 90.57 Hz and delivers state-of-the-art performance on the KITTI benchmark when integrated into the RobMOT framework, achieving a MOTA of 92.27\%.