supervision/examples/speed_estimation

speed estimation

👋 hello

This example performs speed estimation analysis using various object-detection models and ByteTrack - a simple yet effective online multi-object tracking method. It uses the supervision package for multiple tasks such as tracking, annotations, etc.

https://github.com/roboflow/supervision/assets/26109316/d50118c1-2ae4-458d-915a-5d860fd36f71

[!IMPORTANT] Adjust the SOURCE and TARGET configuration if you plan to run a speed estimation script on your video file. Those must be adjusted separately for each camera view. You can learn more from our YouTube tutorial.

💻 install

• clone repository and navigate to example directory

  git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
  cd supervision/examples/speed_estimation
  

• setup python environment and activate it [optional]

  uv venv
  source .venv/bin/activate
  

• install required dependencies

  uv pip install -r requirements.txt
  

• download vehicles.mp4 file

  python video_downloader.py
  

🛠️ script arguments

• --roboflow_api_key (optional): The API key for Roboflow services. If not provided directly, the script tries to fetch it from the ROBOFLOW_API_KEY environment variable. Follow this guide to acquire your API KEY.

• --model_id (optional): Designates the Roboflow model ID to be used. The default value is "yolov8x-1280".

• --source_weights_path: Required. Specifies the path to the YOLO model's weights file, which is essential for the object detection process. This file contains the data that the model uses to identify objects in the video.

• --source_video_path: Required. The path to the source video file that will be analyzed. This is the input video on which traffic flow analysis will be performed.

• --target_video_path: The path to save the output video with annotations. If not specified, the processed video will be displayed in real-time without being saved.

• --confidence_threshold (optional): Sets the confidence threshold for the YOLO model to filter detections. Default is 0.3. This determines how confident the model should be to recognize an object in the video.

• --iou_threshold (optional): Specifies the IOU (Intersection Over Union) threshold for the model. Default is 0.7. This value is used to manage object detection accuracy, particularly in distinguishing between different objects.

⚙️ run

• yolo-nas

  python yolo_nas_example.py \
      --source_video_path data/vehicles.mp4 \
      --target_video_path data/vehicles-result.mp4 \
      --confidence_threshold 0.3 \
      --iou_threshold 0.5
  

• inference

  python inference_example.py \
      --roboflow_api_key "ROBOFLOW_API_KEY" \
      --source_video_path data/vehicles.mp4 \
      --target_video_path data/vehicles-result.mp4 \
      --confidence_threshold 0.3 \
      --iou_threshold 0.5
  

• ultralytics

  python ultralytics_example.py \
      --source_video_path data/vehicles.mp4 \
      --target_video_path data/vehicles-result.mp4 \
      --confidence_threshold 0.3 \
      --iou_threshold 0.5
  

© license

This demo integrates two main components, each with its own licensing:

• ultralytics: The object detection model used in this demo, YOLOv8, is distributed under the AGPL-3.0 license. You can find more details about this license here.

• supervision: The analytics code that powers the zone-based analysis in this demo is based on the Supervision library, which is licensed under the MIT license. This makes the Supervision part of the code fully open source and freely usable in your projects.