下面是一些与上述提到的多目标跟踪技术相关的GitHub仓库推荐，涵盖了 DeepSORT、FairMOT、ReID、Tracktor 等经典方法：
1. DeepSORT
- 仓库地址 : DeepSORT Repository
- 简介: DeepSORT 是一种结合了卡尔曼滤波和外观特征（通过深度学习提取）的跟踪算法，能够在多目标跟踪中保持较高的 ID 保持率，尤其在有遮挡的情况下表现良好。
- 功能: 实现了基于外观和运动信息的目标跟踪。
2. FairMOT
- 仓库地址 : FairMOT Repository
- 简介: FairMOT 是一个结合了多目标检测和跟踪的实时多目标跟踪算法，通过结合外观信息和中心点检测，在遮挡情况下仍能保持良好的跟踪效果。
- 功能: 实现多目标检测与跟踪一体化，效果精度高，支持行人、车辆等多目标类型。
3. JDE (Joint Detection and Embedding)
- 仓库地址 : JDE Repository
- 简介: JDE 是一种同时进行目标检测和外观特征嵌入的多目标跟踪算法，可以更高效地处理遮挡和 ID 保持问题。
- 功能: 实现了端到端的检测和重识别（ReID）模型，非常适合大规模多目标跟踪任务。
4. Tracktor
- 仓库地址 : Tracktor Repository
- 简介: Tracktor 通过直接扩展目标检测器中的边界框回归模块来进行跟踪，提供了简单而有效的目标跟踪方案，特别是应对遮挡时的目标身份保持。
- 功能: 使用现成的检测器和回归信息进行跟踪，提升了遮挡恢复的能力。
5. CenterTrack
- 仓库地址 : CenterTrack Repository
- 简介: CenterTrack 通过预测目标中心点和运动轨迹来进行多目标跟踪，适用于处理遮挡问题和目标重新出现时的 ID 关联。
- 功能: 提供了实时的多目标跟踪方案，并且对复杂场景中的遮挡问题有较好的处理能力。
6. GNN-based Tracking
- 仓库地址 : GNN MOT Repository
- 简介: 该项目基于图神经网络（GNN）进行多目标跟踪，通过建模目标间的关联和互动，在遮挡和复杂场景下表现出色。
- 功能: 利用图神经网络处理多目标跟踪任务，特别适用于多目标之间关联性强的场景。
这些 GitHub 仓库提供了多目标跟踪的不同实现，涵盖了常见的遮挡处理和 ID 保持技术。你可以根据需求选择相应的仓库进行使用和研究。
Multi-Object Tracking (MOT) Technologies Analysis Report
| Technique | Offline Deployment | Fine-tuning | Quality | Performance Rating (Out of 5) | Effectiveness (Occlusion Handling, ID Switch) | Remarks |
|---|---|---|---|---|---|---|
| YOLOv10-DeepSORT | Yes | Yes | High | 4.5 | Handles occlusion well, low ID switches【12†source】 | Combines state-of-the-art detection (YOLOv10) with DeepSORT tracking, ideal for real-time applications. High accuracy. |
| YOLOv5-DeepSORT | Yes | Yes | High | 4.3 | Good occlusion handling but fewer ID switches than FairMOT【14†source】【15†source】 | Popular for tracking, especially with YOLOv5's lightweight architecture. DeepSORT ensures robust ID preservation during occlusion. |
| FairMOT | Yes | Yes | Very High | 4.8 | Best for occlusion and low ID switch【14†source】【15†source】 | Real-time detection and tracking in a single framework, achieves excellent results in complex scenes with fewer ID losses. |
| Tracktor | Yes | Yes | Medium | 4.0 | Works well for short-term occlusions, but may struggle with extended ones | Combines tracking with object detector's bounding box regressor, but less robust in very crowded scenes. |
| CenterTrack | Yes | Yes | High | 4.2 | Good ID switch recovery, but can struggle with complex occlusions | Uses center points and heatmap regression for tracking, suitable for real-time scenarios with moderate complexity【14†source】. |
Summary of Key Points:
- Offline Deployment: All methods can be deployed offline, making them suitable for a variety of environments.
- Fine-tuning: Each of these methods allows for fine-tuning, enabling them to adapt to specific datasets and improve tracking performance.
- Quality: FairMOT is currently leading in quality due to its anchor-free approach and integrated tracking and detection framework, providing superior ID consistency in complex environments.
- Performance: FairMOT and YOLOv10-DeepSORT outperform others in scenarios involving frequent occlusions, with low ID switches and better tracking precision.
- Effectiveness: FairMOT stands out for its ability to handle difficult scenarios, with fewer ID switches and strong occlusion handling, followed closely by YOLOv5-DeepSORT, which excels in real-time applications.
FairMOT remains a top choice for challenging multi-object tracking tasks, especially when occlusion is a concern.
Here are some of the latest and widely-used GitHub repositories and techniques for multi-object tracking (MOT) that address the problem of maintaining object IDs even after occlusions:
-
YOLOv10 with DeepSORT: This repository combines the YOLOv10 detector with DeepSORT for robust object detection and tracking. DeepSORT is particularly known for handling occlusions better by integrating appearance descriptors with motion-based tracking, reducing ID switches. This setup is ideal for real-time applications【12†source】.
- Repository: YOLOv10-DeepSORT.
-
YOLOv5 with DeepSORT: Another popular combination, this repo integrates YOLOv5 with DeepSORT for multi-object tracking. DeepSORT's advanced cosine metric learning helps in recovering object identities after occlusion, making it a reliable option【13†source】【14†source】.
- Repository: YOLOv5-DeepSORT-Pytorch.
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FairMOT: FairMOT is a modern anchor-free approach that tackles object detection and Re-ID in a unified framework. Unlike the two-stage tracker like YOLOv5 + DeepSORT, FairMOT processes detection and tracking simultaneously, offering high precision with fewer identity switches, particularly in complex scenes【14†source】【15†source】.
- Repository: FairMOT.
These repositories are actively maintained and reflect some of the latest advancements in MOT, addressing issues like object occlusion, ID recovery, and real-time performance.