xCalibre is an advanced, real-time, image recognition system. It offers businesses, governments, militaries, and private clients smart detection and recognition functions with CCTV, recorded videos, and images. xCalibre makes it possible to collect and analyze information in real time, identify humans and objects, and send alerts in defined cases.
The system is AI-based and can make predictions and recommendations for optimization based on real-time statistics and historical data. xCalibre identifies humans, with or without facial and body covers, and concealed objects as well. The system includes an advanced cybersecurity protocol to keep all data secure and confidential.
xCalibre Technology Overview
xCalibre is an advanced image-analysis technology, with intelligent detection and recognition functions, that allows a wide array of users to enhance the capabilities of real-time video and CCTV systems. xCalibre makes it possible to analyze real-time cameras, recorded video, and images and identify humans and objects for further processing. The system optimizes data and, using its neural network-based analysis, builds a database, keeps history, and enables crucial security alerts – in real-time of course. The system’s algorithms are point detector-based.
They can identify points in an image with 2D changes. A geometric-feature evaluator overlays at least one mesh on an image and analyzes features on at least one mesh. An internal calibrator transforms data from the point detector and the geometric-feature evaluator into a 3D point figure of the image. A depth evaluator determines the final shape. A 3D object model of the image (face, body, or object) is constructed.
The system can construct and learn features on a partial view, e.g. where a face is to some extent covered. xCalibre includes an AI analysis that works in phases. In the first phase, a high-resolution, pixelation-based image mapping is performed using a neural network and associated neural network analysis. A second phase uses a proprietary convolutional neural network (CNN) named First Encounter (FECNN). First Encounter performs low-resolution, pixilation-based mapping of an image (face body, or object).
In the third phase, portrait-wide mapping is performed using the neural network. In phase four, sideways mapping using an expert system can also be run. In phase five, a biometric facial mapping is performed using the expert system. Phase six entails a human body style study based on AI vector mapping. Finally, phase seven, based on AI-vector mapping, identifies clothing, face covering, and accessories. A person of interest (or object) can be identified. For humans, complete identification can be done with or without clothing or face cover.
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