用场景级一致性修复热成像行人轨迹断裂，不依赖复杂重识别模型。
Understanding Identity Continuity in Thermal Video through Scene-Level Consistency
- 基于时空、运动和边界线索，设计轻量级轨迹修复模块。
- IDF1提升至84.93，仅通过保守重连实现，不损失MOTA。
- 适合热成像行人跟踪，尤其在低信息场景下表现优异。
热成像行人多目标跟踪因外观线索弱、检测频繁中断，导致轨迹严重碎片化。本文研究是否可通过轻量级后处理恢复身份连续性，而无需依赖复杂的重识别模型或在线关联机制。以YOLOv8与SORT为基础，引入模块化身份修复后端，包含在线短间隙重映射和离线轨迹片段重连，利用时间、空间、运动及边界线索。在固定验证集上的受控消融实验及官方PBVS热成像行人跟踪基准测试显示，主要身份提升来自保守重连策略，使IDF1从82.25提升至84.93，同时保持MOTA稳定；多种启发式阈值在广泛参数范围内表现稳健。结果表明，在低信息热成像中，通过高精度轨迹重连实现身份恢复，比增加追踪器复杂度更有效。该研究为热成像视频中的身份连续性提供了可控分析，证明场景级时空一致性对身份连续性的主导作用，优于局部帧间关联。
原文摘要 · Abstract (English)
Thermal pedestrian MOT remains challenging because weak appearance cues and frequent detection interruptions cause severe trajectory fragmentation. We study whether lightweight post-processing can recover identity continuity without relying on heavy re-identification models or complex online association. Starting from a YOLOv8 and SORT baseline, we add a modular identity-repair backend consisting of online short-gap remapping and offline tracklet relinking based on temporal, spatial, motion, and border cues. Controlled ablations on a fixed validation split and evaluation on the official PBVS Thermal Pedestrian MOT benchmark show that the main identity gains arise from conservative relinking, improving IDF1 from 82.25 to 84.93 while preserving MOTA, whereas many heuristic thresholds remain stable across broad operating ranges. These results suggest that, in low-information thermal imagery, robust identity recovery can be achieved more effectively through high-precision trajectory relinking than through increasing tracker complexity. These results provide a controlled analysis of identity recovery in thermal video, showing that scene-level spatial-temporal consistency plays a dominant role in identity continuity compared to local frame-to-frame association.
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