IoT Case Study | Machine Learning for Asset Management

Client: A India-based company who protect and secure enterprises with advanced systems, both hardware and software.

Domain: IT & Commodity Security Management Company

Offering: To improve the workflow and enable business scalability, automating the process without increasing the human resources involved.

Challenge: The client collects images of the objects maintained using high-quality cameras. The images are collected at big file size, and not all the images contain useful information. This increases the need for a human workforce to filter the images before processing.

The Solution: The Borgos team selected a machine learning approach to automate the image filtering process. The solution combines machine learning libraries, image pre-processing, and computer vision algorithms as the key components. The recalibrated models and calculations make it possible to run visual recognition on the resource-limited devices using as little energy as possible. This enables close-to-real-time object tracking.The technology stack includes Hadoop, Scala, Java languages.