OmniTrack++：通过学习大视场轨迹反馈实现全向多目标跟踪
OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback
AI总结：
针对360°全景图像中的多目标跟踪挑战，提出反馈驱动框架OmniTrack++，通过轨迹线索优化感知，并建立EmboTrack基准，在JRDB和QuadTrack上实现HOTA大幅提升。
摘要：
本文研究全景图像中的多目标跟踪（MOT），这带来了独特的挑战，包括360°视场（FoV）、分辨率稀释和严重的视图依赖畸变。为窄视场针孔相机设计的传统MOT方法在这些条件下泛化效果不佳。为了解决360° FoV下的全景畸变、大搜索空间和身份模糊问题，OmniTrack++采用了一个反馈驱动的框架，通过轨迹线索逐步优化感知。DynamicSSM模块首先稳定全景特征，隐式缓解几何畸变。在归一化表示的基础上，FlexiTrack Instances利用轨迹反馈进行灵活定位和可靠的短期关联。为确保长期鲁棒性，ExpertTrack Memory通过混合专家设计整合外观线索，能够从碎片化轨迹中恢复并减少身份漂移。最后，Tracklet Management模块根据场景动态在端到端和检测跟踪模式之间自适应切换，为全景MOT提供平衡且可扩展的解决方案。为支持严格评估，我们建立了EmboTrack基准，这是一个全景MOT综合数据集，包括用四足机器人捕获的QuadTrack和用双足轮腿机器人收集的BipTrack。这些数据集涵盖广角环境和多样的运动模式，为现实世界全景感知提供了具有挑战性的测试平台。在JRDB和EmboTrack上的大量实验表明，OmniTrack++实现了最先进的性能，与原始OmniTrack相比，在JRDB上HOTA提升+25.5%，在QuadTrack上提升+43.07%。数据集和代码将在https://github.com/xifen523/OmniTrack公开发布。
英文摘要：
This paper investigates Multi-Object Tracking (MOT) in panoramic imagery, which introduces unique challenges including a 360° Field of View (FoV), resolution dilution, and severe view-dependent distortions. Conventional MOT methods designed for narrow-FoV pinhole cameras generalize unsatisfactorily under these conditions. 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 +25.5% on JRDB and +43.07% on QuadTrack over the original OmniTrack. Datasets and code will be made publicly available at https://github.com/xifen523/OmniTrack.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
