heatmap and tracking

👋 hello

This script performs heatmap and tracking analysis using YOLOv8, an object-detection method and ByteTrack, a simple yet effective online multi-object tracking method. It uses the supervision package for multiple tasks such as drawing heatmap annotations, tracking objects, etc.

💻 install

• clone repository and navigate to example directory

  git clone https://github.com/roboflow/supervision.git
  cd supervision/examples/heatmap_and_track

• setup python environment and activate it [optional]

  python3 -m venv venv
  source venv/bin/activate

• install required dependencies

  pip install -r requirements.txt

🛠️ script arguments

• --source_weights_path: Required. Specifies the path to the weights file for the YOLO model. This file contains the trained model data necessary for object detection.
• --source_video_path (optional): The path to the source video file that will be analyzed. This is the input video on which crowd analysis will be performed. If not specified default is people-walking.mp4 from supervision assets
• --target_video_path (optional): The path to save the output.mp4 video with annotations.
• --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.
• --heatmap_alpha (optional): Opacity of the overlay mask, between 0 and 1.
• --radius (optional): Radius of the heat circle.
• --track_threshold (optional): Detection confidence threshold for track activation.
• --track_seconds (optional): Number of seconds to buffer when a track is lost.
• --match_threshold (optional): Threshold for matching tracks with detections.

⚙️ run

python script.py \
--source_weights_path weight.pt \
--source_video_path  input_video.mp4 \
--confidence_threshold 0.3 \
--iou_threshold 0.5 \
--target_video_path  output_video.mp4

© 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.