Detects parts of the video that contains text and transcribes them.
Perform real-time monitoring for different objects.
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).
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.
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.
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.
Specify predefined parameters and properties like size, speed, temperature, colour, etc. for object qualification and identification.
Recognize natural speech, detect and describe images, videos by labeling them and transcribing speeches with the help of cloud based APIs
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
Breakdown videos into multiple image frames and use pre trained data through sequence learning to generate a precise description of the real-time videos
Used neural networks and auxiliary audio feeds to train advanced deep models that can identify visual differences of objects in any given environment
Identify moving objects in real-time by sensing data from objects in the environment via tailored machine learning analytics to remove undesired objects
ML based video analytics we built also helps in solving the challenges faced in traffic monitoring.
Offer predictive information to users based on the information detected from the real-time video.
Instantly track objects from live video streams or real-time video footage with improved ML based visual computing platforms.
Detects parts of the video that contains text and transcribes them.
Generate predictive alarms by profiling and tracking objects to prevent unwanted incidents.
“It was impressive to witness the quick turnaround time in completion of the project and the approaches taken by SoluLab’s Machine Learning team to eliminate the bottlenecks faced in building Vega.”