Author: Padmashri Gargesa

Megh’s contextual analytics framework

Contextual analytics refers to the use of data analytics methods that take into account the setting in which data is generated, collected, and analyzed. The goal is to provide a more complete and accurate understanding of the data by considering the context in which it was created. In the field of customer behavior analysis, for

Read More »

Megh VAS Portal: Manage multiple sites through a single pane of glass

Megh’s VAS Portal provides an easy-to-use dashboard that can be accessed via any browser to manage multiple VAS Suite instances. Supported by a powerful microservices cluster on the backend, VAS Suite supports video analytics use cases for public safety, worker safety, and inventory management for deployment in smart buildings, smart warehouses, smart cities, and other

Read More »

Megh VAS SDK

Megh’s fully customizable, cross-platform Video Analytics Solution (VAS) is available as the VAS SDK toolkit and VAS Suite of products. VAS SDK is targeted for enterprises, system integrators (SI), OEMs, and developers, enabling full control to optimize video analytics pipelines and integrate highly customizable AI into applications. VAS SDK is one member of Megh’s family

Read More »

Implementing a CPU+FPGA-based real-time video analytics pipeline

This post is a follow-up to “Implementing a CPU-based real-time video analytics pipeline,” where we discussed a CPU-based end-to-end video analytics pipeline. As seen in that post, a CPU-based pipeline runs into severe performance bottlenecks. Here we discuss how we address and overcome these bottlenecks using FPGAs as hardware accelerators. We explain Megh’s Video Analytics

Read More »

Implementing a CPU-based real-time video analytics pipeline

With growth in available data and computation power, use of video analytics solutions has been growing visibly. Most real-time video analytics use-cases, however, require response times in milliseconds—a level of performance that both CPUs and GPUs cannot always meet when it comes to inference. Here we discuss implementation of a real-time video analytics pipeline on

Read More »

Implementing a real-time, deep learning pipeline with Spark Streaming

With the current information age defining the third wave, we are facing an explosion of real-time data, which is in turn increasing demand for real-time analytics. A real-time analytics solution pipeline typically utilizes a streaming library and an analytics platform. Apache Spark is an open-source, distributed computing platform designed to run analytics payloads on a

Read More »

Categories

Categories