Category: Neuralet

Galliot now hosts all Neuralet’s content and expertise gained in three years of work and completing high-quality applications, mainly in Computer Vision and Deep Learning.

Here is an overview of the Deep-Learning-based Human Action Recognition problem statement. This overview presents some computer vision solutions to this problem and introduces relevant datasets to HAR.

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 part of the Galliot DeepStream articles introduces the most important DeepStream elements, plugins, and functions. Alongside this, we show you how to build a Pipeline and customize your video analytics 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.

Here we briefly described how we deployed our Adaptive Learning object Detection Model on X86s and Jetson devices using NVIDIA DeepStream and Triton Inference Server.

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.

Human Pose Estimation is a Machine Learning task that uses computer vision techniques to identify the body posture of a person.

This article explains how to map pixel distances on 2D images to the corresponding real-world distances in 3D scenes using Homography Estimation and applies this approach to a practical problem as a use case.

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