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
Oil and
Gas industry
Construction
industry
Industrial plant
& equipment firms
Aerospace
Utilities
Retail and Banking

Concentio®
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 DetailConcentio 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.
© 2021 Scry Analytics