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Social Distance Detector

Social Distance Detector Node Documentation

The Social Distance Detector node in AugeLab Studio is used to detect social distancing violations in an input stream or image. It utilizes the YOLOv4 object detection algorithm to detect people and calculates the distances between them to check for violations of social distancing guidelines.

Node Overview

The Social Distance Detector node takes an input stream or image and performs social distance detection. It detects people using the YOLOv4 algorithm, calculates the distances between them, and identifies violations based on a specified distance threshold.

Node Interaction

  1. 1.
    Drag and drop the Social Distance Detector node from the node library onto the canvas in AugeLab Studio.
  2. 2.
    Connect the input stream or image data to the node's input socket.
  3. 3.
    Provide the perspective matrix as an input, if available, to enable distance measurements in real-world units.
  4. 4.
    Set the distance threshold to define the minimum allowed distance between individuals.
  5. 5.
    Run the pipeline or execute the node to process the input stream or image and detect social distancing violations.
  6. 6.
    View the output image with visualizations of the detected people and violations.
  7. 7.
    Retrieve the counts for the number of people detected, the number of violations, and a boolean flag indicating if any violations were detected.

Implementation Details

The Social Distance Detector node uses the YOLOv4 object detection algorithm to detect people in the input stream or image. It follows these steps during its implementation:
  1. 1.
    Initialization:
    • The node initializes the YOLOv4 object detector using pre-trained weights, configuration files, and class labels.
    • The weights, configuration file, and class file for person detection are loaded.
    • The distance threshold is set to define the minimum allowed distance between individuals.
  2. 2.
    User Interface:
    • The node provides a slider widget to adjust the confidence threshold for person detection.
    • The confidence threshold determines the minimum confidence level required for a person to be detected.
    • The node also requires the input of a distance threshold to define the minimum allowed distance between individuals.
  3. 3.
    Social Distance Detection:
    • The node receives an input stream or image and performs person detection using the YOLOv4 detector.
    • It processes the input and detects people based on the pre-trained model.
    • The detected people are used to calculate the distances between individuals.
  4. 4.
    Distance Calculation:
    • If a perspective matrix is provided as an input, the node uses it to transform the detected positions of people to real-world coordinates.
    • The distances between individuals are calculated using the Euclidean distance metric.
    • The measured distances are compared to the specified distance threshold to identify violations of social distancing guidelines.
  5. 5.
    Visualization:
    • The node generates an output image with visualizations of the detected people and social distancing violations.
    • Detected people are represented by circles on the image.
    • Violations of social distancing guidelines are represented by lines connecting individuals who are too close together.
  6. 6.
    Counting and Violation Detection:
    • The node counts the number of people detected in the input stream or image.
    • It also counts the number of violations of social distancing guidelines based on the specified distance threshold.
    • The node provides a boolean flag indicating if any violations were detected.
  7. 7.
    Output:
    • The node outputs the processed image with visualizations of the detected people and violations.
    • It also provides counts for the number of people detected, the number of violations, and a boolean flag indicating if any violations were detected.

Usage

  1. 1.
    Drag and drop the Social Distance Detector node from the node library onto the canvas in AugeLab Studio.
  2. 2.
    Connect the input stream or image data to the node's input socket.
  3. 3.
    Provide the perspective matrix as an input, if available, to enable distance measurements in real-world units.
  4. 4.
    Set the distance threshold to define the minimum allowed distance between individuals.
  5. 5.
    Run the pipeline or execute the node to process the input stream or image and detect social distancing violations.
  6. 6.
    View the output image with visualizations of the detected people and violations.
  7. 7.
    Retrieve the counts for the number of people detected, the number of violations, and a boolean flag indicating if any violations were detected.

Notes

  • The Social Distance Detector node provides a convenient way to detect and count social distancing violations in an input stream or image.
  • It utilizes the YOLOv4 object detection algorithm for accurate and efficient person detection.
  • The node supports the detection of people in various environments and scenarios.
  • The distance threshold can be adjusted to define the minimum allowed distance between individuals for social distancing.
  • The node outputs a processed image with visualizations of the detected people and social distancing violations.
  • It provides counts for the number of people detected, the number of violations, and a boolean flag indicating if any violations were detected.
  • The Social Distance Detector node can be used in various applications, including crowd monitoring, public safety, and compliance monitoring.
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Last modified 4mo ago