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84 lines
4.3 KiB
84 lines
4.3 KiB
--- |
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comments: true |
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description: Learn how to use Multi-Object Tracking with YOLO. Explore dataset formats and see upcoming features for training trackers. Start with Python or CLI examples. |
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keywords: YOLO, Multi-Object Tracking, Tracking Datasets, Python Tracking Example, CLI Tracking Example, Object Detection, Ultralytics, AI, Machine Learning |
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--- |
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# Multi-object Tracking Datasets Overview |
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## Dataset Format (Coming Soon) |
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Multi-Object Detector doesn't need standalone training and directly supports pre-trained detection, segmentation or Pose models. Support for training trackers alone is coming soon |
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## Usage |
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!!! Example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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model = YOLO("yolov8n.pt") |
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results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True) |
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``` |
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=== "CLI" |
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```bash |
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yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3, iou=0.5 show |
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``` |
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## FAQ |
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### How do I use Multi-Object Tracking with Ultralytics YOLO? |
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To use Multi-Object Tracking with Ultralytics YOLO, you can start by using the Python or CLI examples provided. Here is how you can get started: |
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!!! Example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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model = YOLO("yolov8n.pt") # Load the YOLOv8 model |
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results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True) |
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``` |
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=== "CLI" |
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```bash |
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yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3 iou=0.5 show |
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``` |
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These commands load the YOLOv8 model and use it for tracking objects in the given video source with specific confidence (`conf`) and Intersection over Union (`iou`) thresholds. For more details, refer to the [track mode documentation](../../modes/track.md). |
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### What are the upcoming features for training trackers in Ultralytics? |
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Ultralytics is continuously enhancing its AI models. An upcoming feature will enable the training of standalone trackers. Until then, Multi-Object Detector leverages pre-trained detection, segmentation, or Pose models for tracking without requiring standalone training. Stay updated by following our [blog](https://www.ultralytics.com/blog) or checking the [upcoming features](../../reference/trackers/track.md). |
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### Why should I use Ultralytics YOLO for multi-object tracking? |
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Ultralytics YOLO is a state-of-the-art object detection model known for its real-time performance and high accuracy. Using YOLO for multi-object tracking provides several advantages: |
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- **Real-time tracking:** Achieve efficient and high-speed tracking ideal for dynamic environments. |
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- **Flexibility with pre-trained models:** No need to train from scratch; simply use pre-trained detection, segmentation, or Pose models. |
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- **Ease of use:** Simple API integration with both Python and CLI makes setting up tracking pipelines straightforward. |
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- **Extensive documentation and community support:** Ultralytics provides comprehensive documentation and an active community forum to troubleshoot issues and enhance your tracking models. |
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For more details on setting up and using YOLO for tracking, visit our [track usage guide](../../modes/track.md). |
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### Can I use custom datasets for multi-object tracking with Ultralytics YOLO? |
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Yes, you can use custom datasets for multi-object tracking with Ultralytics YOLO. While support for standalone tracker training is an upcoming feature, you can already use pre-trained models on your custom datasets. Prepare your datasets in the appropriate format compatible with YOLO and follow the documentation to integrate them. |
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### How do I interpret the results from the Ultralytics YOLO tracking model? |
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After running a tracking job with Ultralytics YOLO, the results include various data points such as tracked object IDs, their bounding boxes, and the confidence scores. Here's a brief overview of how to interpret these results: |
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- **Tracked IDs:** Each object is assigned a unique ID, which helps in tracking it across frames. |
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- **Bounding boxes:** These indicate the location of tracked objects within the frame. |
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- **Confidence scores:** These reflect the model's confidence in detecting the tracked object. |
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For detailed guidance on interpreting and visualizing these results, refer to the [results handling guide](../../reference/engine/results.md).
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