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.
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
& equipment firms
Retail and Banking
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
- 25+ proprietary AI-based algorithms related to computer vision and deep learning networks
- Scry’s intellectual property in feature engineering & signal selection
- Optimized for the “edge”, with proprietary models trained centrally and output produced locally and close to “end” devices and sensors
- Developed to work in “rugged” environments, e.g. streets, railway stations, open fields, and jungles
- Uses external data (weather, GPS, traffic, etc.) to improve accuracy
- “Human in the loop” helps the software in improving its accuracy on a continuous basis
Concentio has been built using
the following technologies
Generic rule-based machine learning
Big data integration
Big data analytics
Key Differentiators &
Ready to use data ingestion connectors and APIs.
Customizable real-time alerts as per client requirements.
"Inferencing at the edge” allows it to forewarn of a potential mishap in real-time.
90%+ accuracy to detect & connect entities while monitoring a scene in nearby or remote locations.
Reduced unplanned downtime by detecting equipment issues such as cracks and degradation.