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Video Analytics

Video Analytics is an advanced computer vision product that harnesses the power of AI and ML models to provide real-time analysis and insights through camera feeds. This case study offers an in-depth look into the capabilities and impact of this innovative solution in various settings, including old age homes, hospitals, and packaging factories.

Product Features

Video Analytics offers a range of features designed to enhance security and provide valuable insights:
  • Intruder Detection: Utilizes AI models to identify and alert on intruder presence.
  • Weapon Detection: Incorporates machine learning models to identify weapons in camera feeds.
  • Human Footfall Tracking:Monitors and analyzes human traffic, providing insights on visitor patterns.
  • Number Plate Recognition: Utilizes AI to read and record license plates for security and tracking purposes.
  • Facial Recognition:Identifies and verifies individuals through facial recognition technology.
  • Live Polygons: Allows users to draw live polygons or squares on camera feeds to specify areas for activity detection.
  • Technologies Used

    ANGULAR

    Used for the frontend development, creating a responsive and intuitive user interface.

    AI/ML Models

    Trained and fine-tuned by a dedicated team of AI/ML experts to enable the various detection and recognition capabilities.

    NODE JS

    Employed for server-side development, handling real-time data processing and communication with IoT devices.

    R-T Analytics

    Real time analytics provides live graphs and analytics based on the data collected from camera feeds.

    Deployment Settings

    Video Analytics has been deployed as a demo in several key settings, including:
  • Old Age Homes: Enhancing the security and well-being of residents while providing valuable insights into their daily activities.
  • Hospitals: Ensuring a secure environment for both patients and staff and optimizing operational efficiency.
  • Packaging Factories: Boosting security and production process management by tracking human activity and monitoring entry points.
  • Impact and Benefits

    The deployment of Video Analytics has yielded several tangible benefits:
  • Enhanced Security: Intruder detection and weapon recognition capabilities have significantly improved security and threat prevention in all deployment settings.
  • Improved Visitor Management: Number plate recognition and facial recognition technologies have streamlined visitor management and access control.
  • Operational Efficiency: Insights into human footfall and activity have allowed for better resource allocation and process optimization in packaging factories and hospitals.
  • Real-time Insights:Live graphs and analytics enable users to make informed decisions and respond promptly to unfolding situations.
  • Challenges Overcome

    The development and deployment of Video Analytics were not without challenges:
    • Model Training: Training AI/ML models for diverse scenarios and maintaining their accuracy required continuous efforts from the AI/ML team.
    • Privacy and Ethics: Ensuring privacy compliance and ethical use of facial recognition technology demanded careful considerations and configurations.
    • Integration: Integrating the product seamlessly with existing camera systems and networks was a technical challenge.

    Future Directions

    Video Analytics continues to evolve and has promising future directions:
  • Enhanced Models: Ongoing refinement of AI/ML models for better accuracy and coverage.
  • Scalability:Expanding the product’s capacity to handle more cameras and data streams.
  • Customization:Allowing users to create custom detection models for specific use cases.
  • Wider Adoption: Extending deployment to additional industries and settings where security and insights are paramount.
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