多摄像头目标追踪框架，用轨迹和外观匹配实现全局身份一致
Glance-MCMT: A General MCMT Framework with Glance Initialization and Progressive Association
- 先用单相机跟踪，再通过轨迹与特征匹配初始化全局身份
- 后续帧中优先匹配已有身份，仅当无合适匹配时才创建新身份
- 结合3D位置验证，提升跨视角身份一致性
我们提出一种多摄像头多目标追踪框架，通过轨迹和外观线索确保跨视角的全局身份一致。流程始于基于BoT-SORT的单摄像头跟踪，随后进入初始凝视阶段，通过轨迹-特征匹配初始化全局ID。在后续帧中，新轨迹片段通过优先级全局匹配策略与已有全局身份关联。仅当无法找到足够相似的轨迹或特征匹配时，才引入新全局ID。利用深度图与标定信息估计3D位置，进行空间有效性验证。
原文摘要 · Abstract (English)
We propose a multi-camera multi-target (MCMT) tracking framework that ensures consistent global identity assignment across views using trajectory and appearance cues. The pipeline starts with BoT-SORT-based single-camera tracking, followed by an initial glance phase to initialize global IDs via trajectory-feature matching. In later frames, new tracklets are matched to existing global identities through a prioritized global matching strategy. New global IDs are only introduced when no sufficiently similar trajectory or feature match is found. 3D positions are estimated using depth maps and calibration for spatial validation.
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