Talk to an Expert

Mobile App

Blockchain Development

Vega2023-02-15T19:27:36+05:30

The system we built could monitor the behavior of objects with its movement patterns to analyze the insights from a live video. Vega is a smart real-time video analytics program. It converts actionable data into meaningful information with the help of Machine Learning (ML).

Business Overview

The goal of our client was to democratize video analytics. They wanted to build a machine learning system that can intelligently extract value from videos and convert them into purposeful data.
With the help of ML algorithms, Vega generates directive information about objects identified in the real-time video and sends it to the user’s screen.
This smart data analytics focuses on detecting the valid motion of objects and lighting changes. It also filters out unnecessary noise with the help of intuitive data fed via supervised learning.

The Challenge

Accurate profiling of users with facial recognition features to read facial emotions appropriately.
The vast requirement in appropriate back-end data that the ML system can use to detect necessary features in the real-time video automatically.
Precise processing of the sentiments in a video through sentiment analysis to provide quality motion analytics.

Our Approach

We reserved digital rights management to provide uninterrupted real-time video analysis. A pool of data scientists and machine learning experts invested significant amounts of their time in labeling data appropriately. This labeled data helps Vega in accurately identifying objects and displaying necessary information in real-time accordingly.

USP of our Project

Object Specification

Specify predefined parameters and properties like size, speed, temperature, colour, etc. for object qualification and identification.

Cloud Vision

Recognize natural speech, detect and describe images, videos by labeling them and transcribing speeches with the help of cloud based APIs

Bi-Spectral Analysis

Used thermal imaging to enhance the quality of detecting objects in images and videos. We improved object detection even in the dark with this approach

Video Classification

Breakdown videos into multiple image frames and use pre trained data through sequence learning to generate a precise description of the real-time videos

Tracking Objects

Used neural networks and auxiliary audio feeds to train advanced deep models that can identify visual differences of objects in any given environment

Edge Computing

Identify moving objects in real-time by sensing data from objects in the environment via tailored machine learning analytics to remove undesired objects

Project Highlights

ML based video analytics we built also helps in solving the challenges faced in traffic monitoring.