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
As deep learning (DL) becomes more pervasive, the need for efficient and fast computation is increasing. Traditional central processing units (CPUs) and graphical processing units (GPUs) are typically used for acceleration, despite the limiting nature of their fixed architectures. Field programmable gate arrays (FPGAs) have been highlighted for their flexibility, but until recently have fallen
Retail inventory loss (or “shrinkage”) is a serious problem, totaling about $100 billion annually—almost 1.8% of sales—worldwide. The issue is even more acute for those moving high-dollar goods, such as fashion and accessories. As traditional retailers grapple with ongoing market concentration, loss of market share to online sellers, and other pressures, there’s good news: Advanced