Category: Edge AI

Overview of the latest releases in NVIDIA DeepStream (6.2), highlighting new features such as the upgraded object tracking library and Graph Composer GUI.

This article discusses using motion detection and pose estimation models for designing an automatic fall detection system for real-world applications.

This is the third article in our series on Nvidia DeepStream. So far, we have introduced DeepStream and its basic concepts. Presented here is a real-world example of how you can use this tool to make your own applications.

This article will walk us through the path of developing an Edge Device-friendly Pose Estimation model for real-time applications such as fall detection, physiotherapy, workspace safety, games, occupancy controls, etc.

In this tutorial, you will find a step-by-step guide on how to use Galliot’s new Adaptive Learning edge vision Service and build a specialized lightweight Object Detection model for your environment.

In this post, we will walk through the steps to run an OpenPifPaf Pose Estimation model on NVIDIA Jetson platforms.

This tutorial provides a technical overview of the latest codebase architecture of the Galliot’s Smart Social Distancing application powered by AI on Edge.

In this article, we will walk through the steps for building a face-mask detector to detect faces in real-world CCTV videos captured in crowded areas.

In this article, we cover the mask detection problem, details about our synthetic ddataset, and classifier that can identify if people are wearing a face mask or not in real-world scenarios.

Adaptive learning builds robust systems that adapt to novel data distributions without having to store or transfer data from edge devices to other systems.

In this post, we explain how we deployed an SSD MobileNet TensorFlow model on NVIDIA Jetson Nano using the TensorFlow Object Detection API.