Megh and Intel® performed a performance and validation assessment of Megh Video Analytics Solution (VAS) running on Intel® NUC kits with 11th or 12th generation Intel® Core™ processors. The kits are cost-effective, small-form factor hardware that provide customers with the performance, power, and accuracy they need to run Megh VAS at the edge. Results demonstrate
The explosion of smart devices, high-bandwidth networks like 5G, and the need to capture and discern data closer to the source have led to the need for a unified edge-analytics application development framework. Devices at the edge, however, are diverse, with each providing their own optimized framework. This makes application portability a big challenge. What
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
Continuous training (CT) of artificial intelligence (AI) models refers to the ongoing process of fine tuning pre-trained AI models with new data, allowing them to continually adapt and improve. This approach is used to keep models up to date with the latest information, trends, and patterns. This results in more accurate predictions and decisions from
The following piece is from insight.tech, an Intel-sponsored publication leveraging the Intel® Partner Alliance to provide business and technical decision-makers the latest and greatest technology trends and business solutions in the IoT space. Fixed-to-mobile, AI-powered video analytics for buildings By Pedro Pereira. November 16, 2022. Smart buildings deliver continuous streams of data from sensors, cameras,
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
With the release of Megh VAS 100 comes a new addition to the Megh Computing solution stack: the Nimble application framework. Nimble is a fast and lightweight service-based framework for implementing CPU, GPU, and FPGA video analytics pipelines. As illustrated below, Nimble sits on top of Arka, Sira, and Deep Learning Engine (DLE), enabling seamless
Video analytics use cases Enterprise users are increasingly interested in implementing complex video analytics use cases that provide business value beyond typical applications. These involve multi-stage models for object detection and image classification with custom trained models that are integrated to solve business problems. Some examples include: Segment Use case Deep learning tasks Retail Cashier-less
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
Four months into 2020, the world is facing a grave global health crisis: the outbreak of a novel coronavirus respiratory disease, COVID-19. Can new digital technology be used to help identify and mitigate the impact of COVID-19? Thankfully, the answer is yes. One technology now in the forefront of the global emergency is the use