只在必要时提取行人重识别特征，提升追踪效率与准确率
When to Extract ReID Features: A Selective Approach for Improved Multiple Object Tracking
- 仅在遮挡等关键时刻才提取ReID特征，减少计算开销
- 在MOT17、MOT20和DanceTrack上保持高精度并显著降低运行时间
- 适用于需兼顾速度与准确性的实际部署场景
许多先进多目标追踪（MOT）方法依赖行人重识别（ReID）特征提取与匹配，尤其在频繁或长时间遮挡下表现优异。尽管端到端检测与追踪成为研究主流，但在MOT17和MOT20等基准上仍未超越传统分离式方法。从应用角度，分离检测与嵌入的方法仍具更高准确率、模块化与实现便利性优势，但计算开销大，不适用于边缘设备。本文提出一种选择性特征提取策略，在保留准确性、模块化与易实现性的前提下，显著降低特征提取开销。该方法可集成至多种SOTA模型，如StrongSORT和Deep OC-SORT。在MOT17、MOT20和DanceTrack上的实验表明，该机制在遮挡期间有效保留特征优势的同时大幅减少运行时间，并通过避免特征匹配阶段混淆，提升对形变与外观相似情况的处理能力，尤其在DanceTrack数据集上表现突出。
原文摘要 · Abstract (English)
Extracting and matching Re-Identification (ReID) features is used by many state-of-the-art (SOTA) Multiple Object Tracking (MOT) methods, particularly effective against frequent and long-term occlusions. While end-to-end object detection and tracking have been the main focus of recent research, they have yet to outperform traditional methods in benchmarks like MOT17 and MOT20. Thus, from an application standpoint, methods with separate detection and embedding remain the best option for accuracy, modularity, and ease of implementation, though they are impractical for edge devices due to the overhead involved. In this paper, we investigate a selective approach to minimize the overhead of feature extraction while preserving accuracy, modularity, and ease of implementation. This approach can be integrated into various SOTA methods. We demonstrate its effectiveness by applying it to StrongSORT and Deep OC-SORT. Experiments on MOT17, MOT20, and DanceTrack datasets show that our mechanism retains the advantages of feature extraction during occlusions while significantly reducing runtime. Additionally, it improves accuracy by preventing confusion in the feature-matching stage, particularly in cases of deformation and appearance similarity, which are common in DanceTrack. https://github.com/emirhanbayar/Fast-StrongSORT, https://github.com/emirhanbayar/Fast-Deep-OC-SORT
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