Real-Time Scene Detection

Computer Vision Algorithm Based Object Recognition

Real-Time Scene Detection algorithms work on millions of images and videos gathered from “edge devices” to reduce latency in real-time scene detection use cases.

Use Cases

  • Helps in adherence to health and safety standards

  • Remote inspection of distributed assets through aerial images

  • Improves safety and security in plant operations

  • Improves customer experience in retail stores, banks, and in-person shopping areas

  • Helps in ensuring Environment, Sustainability and Governance (ESG) regulations, e.g., by analyzing pictures and videos from drones and satellites

Concentio Serves


Deep Learning based Object Detection Application

Real-Time Scene Detection application by Scry Analytics exploits parallel and distributed computing, thereby making them extremely scalable and extendable. Identifies gaps and provides recommendations to enable users to quickly identify and characterize unmitigated high-risk areas.

More Detail

Concentio has been built using
the following technologies

  • Bayesian networks

  • Reinforcement learning

  • Generic rule-based machine learning

  • Heuristic algorithms

  • Data mining

  • Big data integration

  • Big data analytics

  • Reverse engineering

Key Differentiators &
Business Benefits

  • Pluggable APIs

    Ready to use data ingestion connectors and APIs.

  • Customizable Alerts

    Customizable real-time alerts as per client requirements.

  • Failure Forecasting

    "Inferencing at the edge” allows it to forewarn of a potential mishap in real-time.

  • Accuracy

    90%+ accuracy to detect & connect entities while monitoring a scene in nearby or remote locations.

  • Reduced Downtime

    Reduced unplanned downtime by detecting equipment issues such as cracks and degradation.