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# Speed Estimation using Ultralytics YOLO11 🚀

## What is Speed Estimation?

Speed estimation is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using Ultralytics YOLO11 you can now calculate the speed of objects using object tracking alongside distance and time data, crucial for tasks like traffic monitoring and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.

Watch: Speed Estimation using Ultralytics YOLO11

Check Out Our Blog

For deeper insights into speed estimation, check out our blog post: Ultralytics YOLO11 for Speed Estimation in Computer Vision Projects

## Advantages of Speed Estimation

- Efficient Traffic Control: Accurate speed estimation aids in managing traffic flow, enhancing safety, and reducing congestion on roadways.
- Precise Autonomous Navigation: In autonomous systems like self-driving cars, reliable speed estimation ensures safe and accurate vehicle navigation.
- Enhanced Surveillance Security: Speed estimation in surveillance analytics helps identify unusual behaviors or potential threats, improving the effectiveness of security measures.

## Real World Applications

Transportation | Transportation
--- | ---
Speed Estimation on Road using Ultralytics YOLO11 | Speed Estimation on Bridge using Ultralytics YOLO11

Speed is an Estimate

Speed will be an estimate and may not be completely accurate. Additionally, the estimation can vary on camera specifications and related factors.

Speed Estimation using Ultralytics YOLO

CLI Python

    # Run a speed example
    yolo solutions speed show=True

    # Pass a source video
    yolo solutions speed source="path/to/video.mp4"

    # Adjust meter per pixel value based on camera configuration
    yolo solutions speed meter_per_pixel=0.05

    import cv2

    from ultralytics import solutions

    cap = cv2.VideoCapture("path/to/video.mp4")
    assert cap.isOpened(), "Error reading video file"

    # Video writer
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
    video_writer = cv2.VideoWriter("speed_management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

    # Initialize speed estimation object
    speedestimator = solutions.SpeedEstimator(
        show=True,  # display the output
        model="yolo11n.pt",  # path to the YOLO11 model file.
        fps=fps,  # adjust speed based on frame per second
        # max_speed=120,  # cap speed to a max value (km/h) to avoid outliers
        # max_hist=5,  # minimum frames object tracked before computing speed
        # meter_per_pixel=0.05,  # highly depends on the camera configuration
        # classes=[0, 2],  # estimate speed of specific classes.
        # line_width=2,  # adjust the line width for bounding boxes
    )

    # Process video
    while cap.isOpened():
        success, im0 = cap.read()

        if not success:
            print("Video frame is empty or processing is complete.")
            break

        results = speedestimator(im0)

        # print(results)  # access the output

        video_writer.write(results.plot_im)  # write the processed frame.

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()  # destroy all opened windows

### `SpeedEstimator` Arguments

Here's a table with the `SpeedEstimator` arguments:

Argument | Type | Default | Description
--- | --- | --- | ---
`model` | `str` | `None` | Path to Ultralytics YOLO Model File.
`fps` | `float` | `30.0` | Frames per second used for speed calculations.
`max_hist` | `int` | `5` | Maximum historical points to track per object for speed/direction calculations.
`meter_per_pixel` | `float` | `0.05` | Scaling factor used for converting pixel distance to real-world units.
`max_speed` | `int` | `120` | Maximum speed limit in visual overlays (used in alerts).

The `SpeedEstimator` solution allows the use of `track` parameters:

Argument | Type | Default | Description
--- | --- | --- | ---
`tracker` | `str` | `'botsort.yaml'` | Specifies the tracking algorithm to use, e.g., `bytetrack.yaml` or `botsort.yaml`.
`conf` | `float` | `0.3` | Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives.
`iou` | `float` | `0.5` | Sets the Intersection over Union (IoU) threshold for filtering overlapping detections.
`classes` | `list` | `None` | Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes.
`verbose` | `bool` | `True` | Controls the display of tracking results, providing a visual output of tracked objects.
`device` | `str` | `None` | Specifies the device for inference (e.g., `cpu`, `cuda:0` or `0`). Allows users to select between CPU, a specific GPU, or other compute devices for model execution.

Additionally, the following visualization options are supported:

Argument | Type | Default | Description
--- | --- | --- | ---
`show` | `bool` | `False` | If `True`, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing.
`line_width` | `None or int` | `None` | Specifies the line width of bounding boxes. If `None`, the line width is automatically adjusted based on the image size. Provides visual customization for clarity.
`show_conf` | `bool` | `True` | Displays the confidence score for each detection alongside the label. Gives insight into the model's certainty for each detection.
`show_labels` | `bool` | `True` | Displays labels for each detection in the visual output. Provides immediate understanding of detected objects.

## FAQ

### How do I estimate object speed using Ultralytics YOLO11?

Estimating object speed with Ultralytics YOLO11 involves combining object detection and tracking techniques. First, you need to detect objects in each frame using the YOLO11 model. Then, track these objects across frames to calculate their movement over time. Finally, use the distance traveled by the object between frames and the frame rate to estimate its speed.

Example:

    import cv2

    from ultralytics import solutions

    cap = cv2.VideoCapture("path/to/video.mp4")
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
    video_writer = cv2.VideoWriter("speed_estimation.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

    # Initialize SpeedEstimator
    speedestimator = solutions.SpeedEstimator(
        model="yolo11n.pt",
        show=True,
    )

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            break
        results = speedestimator(im0)
        video_writer.write(results.plot_im)

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()

For more details, refer to our official blog post.

### What are the benefits of using Ultralytics YOLO11 for speed estimation in traffic management?

Using Ultralytics YOLO11 for speed estimation offers significant advantages in traffic management:

- Enhanced Safety: Accurately estimate vehicle speeds to detect over-speeding and improve road safety.
- Real-Time Monitoring: Benefit from YOLO11's real-time object detection capability to monitor traffic flow and congestion effectively.
- Scalability: Deploy the model on various hardware setups, from edge devices to servers, ensuring flexible and scalable solutions for large-scale implementations.

For more applications, see advantages of speed estimation.

### Can YOLO11 be integrated with other AI frameworks like TensorFlow or PyTorch?

Yes, YOLO11 can be integrated with other AI frameworks like TensorFlow and PyTorch. Ultralytics provides support for exporting YOLO11 models to various formats like ONNX, TensorRT, and CoreML, ensuring smooth interoperability with other ML frameworks.

To export a YOLO11 model to ONNX format:

    yolo export model=yolo11n.pt format=onnx

Learn more about exporting models in our guide on export.

### How accurate is the speed estimation using Ultralytics YOLO11?

The accuracy of speed estimation using Ultralytics YOLO11 depends on several factors, including the quality of the object tracking, the resolution and frame rate of the video, and environmental variables. While the speed estimator provides reliable estimates, it may not be 100% accurate due to variances in frame processing speed and object occlusion.

Note: Always consider margin of error and validate the estimates with ground truth data when possible.

For further accuracy improvement tips, check the Arguments `SpeedEstimator` section.

📅 Created 1 year ago ✏️ Updated 2 months ago

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