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| comments | description | keywords |
|---|---|---|
| true | 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. | YOLO, Multi-Object Tracking, Tracking Datasets, Python Tracking Example, CLI Tracking Example, Object Detection, Ultralytics, AI, Machine Learning |
Multi-object Tracking Datasets Overview
Dataset Format (Coming Soon)
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
Usage
!!! Example
=== "Python"
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True)
```
=== "CLI"
```bash
yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3, iou=0.5 show
```
FAQ
How do I use Multi-Object Tracking with Ultralytics YOLO?
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:
!!! Example
=== "Python"
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt") # Load the YOLOv8 model
results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True)
```
=== "CLI"
```bash
yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3 iou=0.5 show
```
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.
What are the upcoming features for training trackers in Ultralytics?
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 or checking the upcoming features.
Why should I use Ultralytics YOLO for multi-object tracking?
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:
- Real-time tracking: Achieve efficient and high-speed tracking ideal for dynamic environments.
- Flexibility with pre-trained models: No need to train from scratch; simply use pre-trained detection, segmentation, or Pose models.
- Ease of use: Simple API integration with both Python and CLI makes setting up tracking pipelines straightforward.
- Extensive documentation and community support: Ultralytics provides comprehensive documentation and an active community forum to troubleshoot issues and enhance your tracking models.
For more details on setting up and using YOLO for tracking, visit our track usage guide.
Can I use custom datasets for multi-object tracking with Ultralytics YOLO?
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.
How do I interpret the results from the Ultralytics YOLO tracking model?
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:
- Tracked IDs: Each object is assigned a unique ID, which helps in tracking it across frames.
- Bounding boxes: These indicate the location of tracked objects within the frame.
- Confidence scores: These reflect the model's confidence in detecting the tracked object.
For detailed guidance on interpreting and visualizing these results, refer to the results handling guide.