In the era of COVID-19, maintaining social distancing has become vital. Fortunately, with the help of artificial intelligence, we can now monitor distancing protocols effectively using CCTV cameras and drones. This blog will guide you through the process of setting up a Social Distancing Analyzer, which automatically detects adherence to social distancing measures.

Overview of the Social Distancing Analyzer

This AI tool, designed for educational purposes, assists in preventing the spread of coronavirus by leveraging computer vision. It provides real-time analytics on social distancing practices within monitored areas, helping authorities to respond quickly to violations.

Features of the Tool

• Real-time analytics including:
  • Number of people in a specific area
  • Number of people at high risk
  • The extent of risk posed to individuals
• No personal data collection
• Stores video output for review

How It Works: An Analogy

Imagine you are a lifeguard at a busy beach. Your job is to ensure that everyone is keeping a safe distance while enjoying the sun. As you scan the beach, you note how many people are swimming, sunbathing, or playing in the sand. You have a whistle that you can blow when people get too close to each other, signaling them to create space.

In this analogy, the lifeguard represents the Social Distancing Analyzer, with a surveillance camera serving as your eyes. The AI algorithms act as your whistle, alerting users to violations in real-time, enabling authorities to maintain safety at public gatherings.

Installation Steps

Follow these simple steps to set up your Social Distancing Analyzer:

1. Fork the repository and download the code.
2. Download the following files and place them in the same directory:
   • yolov3.cfg
   • yolov3.weights
3. For slower CPUs, consider using yolov3-tiny (link in the code comments).
4. Install all the dependencies as specified in the README file.
5. Run social_distancing_analyser.py or social_distancing_analyser_v2.0.py.

Troubleshooting Ideas

If you encounter any issues during setup or execution, here are a few troubleshooting tips:

• Ensure that all necessary files are downloaded and correctly placed.
• Verify the installation of dependencies. Use package managers to install any missing libraries.
• For issues related to performance, consider using the lighter yolov3-tiny version.
• Make sure that the angle factor is between 0 and 1 in version 2.0, adjusting as necessary according to camera positioning.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.