追踪未见之物：一种在全遮挡和长期遮挡下进行目标跟踪的遮挡鲁棒框架
Tracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion
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中文总结 AI 辅助
提出一种遮挡鲁棒跟踪框架，集成YOLOv11n、卡尔曼滤波和遮挡感知重识别，在OVIS和军事数据集上显著提升MOTA和IDF1，减少身份切换。
中文摘要 AI 辅助
实时多目标跟踪系统在面对全遮挡和长期遮挡时仍然高度脆弱，此时目标会暂时或完全从摄像机视野中消失。传统跟踪器可能会过早终止轨迹，导致身份丢失，并在国防和监视等应用中降低态势感知能力。本工作提出了一种遮挡鲁棒的目标跟踪框架，通过集成YOLOv11n目标检测、卡尔曼滤波运动预测和基于外观的遮挡感知重识别，来维持目标身份和轨迹连续性。该框架由三个阶段组成：目标检测、遮挡期间的位置估计以及目标重现后的身份恢复。在相同条件下，在同一跟踪框架内评估了六种重识别（Re-ID）架构，其中遮挡感知掩码网络（OAMN）取得了最佳整体性能，因此被选入最终流程。该框架在公开的OVIS数据集上与OccluTrack进行了基准比较，在多目标跟踪精度（MOTA）上实现了18.1%的相对提升，在身份F1分数（IDF1）上实现了25.1%的相对提升，同时将身份切换减少了12.8%。在模拟具有长期遮挡的监视和战场环境的定制军事数据集上，该框架实现了0.734的MOTA和0.729的IDF1，相对于OccluTrack分别有14.2%和5.8%的相对提升。该系统在具有挑战性的遮挡条件下展示了强大的跟踪连续性、稳健的身份保持和可靠的轨迹估计，突显了其在国防相关监视应用中，在目标可见性丧失期间需要连续目标跟踪的有效性。
英文摘要
Real-time multi-object tracking systems remain highly vulnerable to full and long-term occlusion, where targets temporarily or completely disappear from the camera's field of view. Conventional trackers may terminate trajectories prematurely, resulting in identity loss and reduced situational awareness in applications such as defense and surveillance. This work proposes an occlusion-robust target tracking framework that maintains target identity and trajectory continuity through the integration of YOLOv11n object detection, Kalman Filter motion prediction, and occlusion-aware appearance-based re-identification. The framework consists of three stages: object detection, position estimation during occlusion, and identity recovery after target reappearance. Six Re-Identification (Re-ID) architectures were evaluated within the same tracking framework under identical conditions, with the Occlusion-Aware Mask Network (OAMN) achieving the best overall performance and therefore selected for the final pipeline. The framework was benchmarked against OccluTrack on the public OVIS dataset, achieving relative improvements of 18.1 percent in Multiple Object Tracking Accuracy (MOTA) and 25.1 percent in Identity F1 Score (IDF1), while reducing identity switches by 12.8 percent. On a custom military dataset simulating surveillance and battlefield-like environments with long-term occlusion, the framework achieved a MOTA of 0.734 and an IDF1 of 0.729, corresponding to relative improvements of 14.2 percent and 5.8 percent over OccluTrack. The system demonstrated strong tracking continuity, robust identity preservation, and reliable trajectory estimation under challenging occlusion conditions, highlighting its effectiveness for defense-related surveillance applications requiring continuous target tracking during visibility loss.
发表机构
- University of Jeddah（吉达大学）
机构由 AI 辅助整理，请以论文原文为准。
