长镜头帆板视频的身份一致性分析：一种领域专用的离线跟踪系统
Identity-Consistent Analysis of Long-Shot Windsurfing Video: A Domain-Specific Offline Tracking System
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中文总结 AI 辅助
针对长镜头帆板视频中目标小、相机大幅移动且易身份混淆的问题，提出一种离线跟踪系统，通过局部轨迹与全局关联实现高精度跟踪，F1达0.937，优于现有方法。
中文摘要 AI 辅助
长镜头帆板视频兼具小目标、大幅相机平移、长时间遮挡以及快速变化的背景等特点。期望的输出并非通用的多目标跟踪轨迹，而是为每位冲浪者生成独立且稳定的、相对于冲浪者的视频；一次错误的身份合并就可能导致原本有用的结果失效。我们提出了一种离线分析系统，该系统检测冲浪者，形成保守的局部轨迹片段，通过相机补偿运动与前景掩蔽的帆色描述符进行全局关联，并利用帆板装备上的两个姿态关键点驱动相对于冲浪者的虚拟相机。跟踪阶段在21个手工重建的开发视频上进行了评估，这些视频包含41,004个保留观测值。在该固定观测协议下，该生产系统达到了0.957的成对精确率、0.918的召回率和0.937的F1分数，而OC-SORT的F1为0.792，BoT-SORT的F1为0.828。与OC-SORT相比，该系统将碎片化多余量从845降至42，但其95条输出轨迹中有9条混合了冲浪者身份，这些错误影响了21个视频中的7个。
英文摘要
Long-shot windsurfing video combines small targets, large camera pans, prolonged overlaps, and rapidly changing backgrounds. The desired output is not a generic MOT trace but a separate, stable rider-relative video for each surfer; one false identity merge can invalidate an otherwise useful result. We present an offline analysis system that detects surfers, forms conservative local tracklets, links them globally with camera-compensated motion and a foreground-masked sail-color descriptor, and uses two pose keypoints on the rig to drive a rider-relative virtual camera. The tracking stage is evaluated on 21 manually reconstructed development videos containing 41,004 retained observations. On this fixed-observation protocol, the production system achieves 0.957 pairwise precision, 0.918 recall, and 0.937 F1, compared with 0.792 F1 for OC-SORT and 0.828 for BoT-SORT. Compared with OC-SORT, it reduces fragmentation excess from 845 to 42, but nine of its 95 output tracks mix rider identities and these errors affect seven of the 21 videos.
