通过轨迹反馈提升全景多目标追踪的稳定性与准确性
OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback
- 引入动态反馈机制，用轨迹信息逐步优化感知结果
- 在JRDB和QuadTrack上分别提升HOTA达+3.94和+15.03
- 适合需要全景环境长期稳定追踪的机器人应用
为应对360°视角下的全景畸变、大搜索空间及身份混淆问题，OmniTrack++采用反馈驱动框架，利用轨迹线索逐步优化感知。DynamicSSM模块先稳定全景特征，隐式缓解几何畸变；在归一化表示基础上，FlexiTrack实例通过轨迹感知反馈实现灵活定位与可靠短时关联。为保障长时鲁棒性，ExpertTrack Memory 采用专家混合设计整合外观线索，支持断裂轨迹恢复并减少身份漂移。最后，Tracklet Management模块根据场景动态自适应切换端到端与检测追踪模式，提供平衡且可扩展的全景多目标追踪方案。为支持严谨评估，构建了EmboTrack基准数据集，包含四足机器人采集的QuadTrack与双足轮式机器人采集的BipTrack，覆盖宽视角环境与多样化运动模式，构成真实世界全景感知的挑战性测试平台。在JRDB与EmboTrack上的大量实验表明，OmniTrack++达到当前最优性能，在JRDB上HOTA提升+3.94，在QuadTrack上提升+15.03，验证了轨迹反馈、自适应模式切换与鲁棒长时记忆的有效性。代码与数据集将开源于https://github.com/xifen523/OmniTrack。
原文摘要 · Abstract (English)
To address panoramic distortion, large search space, and identity ambiguity under a 360° FoV, OmniTrack++ adopts a feedback-driven framework that progressively refines perception with trajectory cues. A DynamicSSM block first stabilizes panoramic features, implicitly alleviating geometric distortion. On top of normalized representations, FlexiTrack Instances use trajectory-informed feedback for flexible localization and reliable short-term association. To ensure long-term robustness, an ExpertTrack Memory consolidates appearance cues via a Mixture-of-Experts design, enabling recovery from fragmented tracks and reducing identity drift. Finally, a Tracklet Management module adaptively switches between end-to-end and tracking-by-detection modes according to scene dynamics, offering a balanced and scalable solution for panoramic MOT. To support rigorous evaluation, we establish the EmboTrack benchmark, a comprehensive dataset for panoramic MOT that includes QuadTrack, captured with a quadruped robot, and BipTrack, collected with a bipedal wheel-legged robot. Together, these datasets span wide-angle environments and diverse motion patterns, providing a challenging testbed for real-world panoramic perception. Extensive experiments on JRDB and EmboTrack demonstrate that OmniTrack++ achieves state-of-the-art performance, yielding substantial HOTA improvements of +3.94 on JRDB and +15.03 on QuadTrack over the original OmniTrack. These results highlight the effectiveness of trajectory-informed feedback, adaptive paradigm switching, and robust long-term memory in advancing panoramic multi-object tracking. Datasets and code will be made available at https://github.com/xifen523/OmniTrack.
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