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.

翻译：为应对360°视场下的全景畸变、大搜索空间及身份歧义问题，OmniTrack++采用反馈驱动框架，通过轨迹线索逐步优化感知能力。首先，DynamicSSM模块稳定全景特征，隐式缓解几何畸变；在归一化表征基础上，FlexiTrack实例利用轨迹信息反馈实现灵活定位与可靠短期关联。为保障长期鲁棒性，ExpertTrack记忆模块通过混合专家设计整合外观线索，修复碎片化轨迹并减少身份漂移。最终，Tracklet Management模块根据场景动态自适应切换端到端与追踪-检测模式，为全景多目标跟踪提供可扩展的平衡方案。为支撑严格评估，我们构建EmboTrack基准数据集，该全景多目标跟踪综合数据集包含四足机器人平台采集的QuadTrack及双轮腿机器人平台采集的BipTrack子集，覆盖广角环境与多样化运动模式，构成具有挑战性的真实全景感知测试床。在JRDB与EmboTrack上的大量实验表明，OmniTrack++相较原始版本在JRDB上HOTA指标提升3.94%，在QuadTrack上提升15.03%，达到前沿性能。这些结果突显了轨迹信息反馈、自适应范式切换与鲁棒长期记忆在全景多目标跟踪中的有效性。数据集与代码将发布于https://github.com/xifen523/OmniTrack。