The alwaysAI Blog

Computer Vision, IoT and Depth Cameras, Highlighting alwaysAI User Abhijeet Bhatikar

How an alwaysAI user is using computer vision with IoT to solve logistics problems.

As always, we are proud of our users and what they are accomplishing by using alwaysAI to integrate Computer Vision into their projects. This week we would like to highlight our user Abhijeet Bhatikar who is using alwaysAI as part of his project for the OpenCV Spatial AI Competition. Read on to find out more about how he is combining alwaysAI, computer vision and depth cameras to help solve logistics...

Simplifying the Development of Computer Vision Applications for the Edge

Recent trends in computation and automation highlight a massive and sustained growth for computer vision based applications on the edge. Market research also points to tremendous interest in these applications amongst entrepreneurs and developers alike. However, developing commercial-grade CV applications for the edge is hard (link to the new article here). There are two primary reasons for this.

Computer Vision on the Edge

Overcoming Challenges in Bringing CV Applications to Production

Developing a Computer Vision (CV) application and bringing it to production requires  integrating several pieces of hardware and software. How can we ensure the pieces work seamlessly together? With the right methodologies, we can expedite development and deployment of CV applications. It is essential to find a platform with the goal of helping developers create computer vision applications from scratch quickly and easily with...

Take Your Computer Vision App to the Edge

alwaysAI makes building and deploying Computer Vision Apps Easy

At alwaysAI we have the singular mission of making the process of building and deploying computer vision apps to edge devices as easy as possible. That includes training your model, building your app, and deploying your app to edge devices such as the Raspberry Pi, Jetson Nano, and many others. alwaysAI apps are built in Python and can run natively on Mac and Windows, and in our containerized edge runtime environment optimized...

How a teacher and his students are using robotics & alwaysAI to help with COVID-19.

                                                                                       

 

How To Get Started with the NVIDIA Jetson TX2 on alwaysAI

The Jetson TX2 is part of NVIDIA’s line of embedded AI modules enabling super fast computation on the edge. The TX2 is a leg up compared to the Nano and will give you faster inferencing times in your AI applications. In fact, the Jetson TX2 is the fastest, most power-efficient embedded AI computing device. This 7.5 watt supercomputer on a module brings true AI computing at the edge. 

Please note: This setup guide can only be followed if you have a Linux computer. VM support is un-verified.

Deploying with Balena

This short guide will show you how to combine alwaysAI and Balena to easily deploy a computer vision application to multiple devices with a single command.

Transform Your Business with Computer Vision

Computer vision (CV) is a huge part of Industry 4.0 and the changing technological landscape as we know it. Computer vision will allow deeper, more impactful insights into businesses in all sectors. Healthcare providers will be able to more quickly and safely diagnose and treat patients. Manufacturing operations will have enhanced security and productivity. Companies looking for more security while operating virtually, can use computer vision to keep track of their assets, and assure the...

How to Recognize Human Activity Using alwaysAI

The ability to recognize human activity with computer vision allows us to create applications that can interact with and respond to a user in real time. For instance, we can make an application that gives feedback to a user in the moment so that they can learn how to recreate the perfect golf swing, or that sends an immediate alert for help when someone has fallen, or that generates an immersive augmented reality experience based on the user's position.

Getting Started with the Raspberry Pi 3B+ and 4

The Raspberry Pi 3B+ and the Raspberry Pi 4 are ubiquitous among the hobbyist community of developers. They are reliable, easy to use single-board computers (SBCs) that are very affordable, making it easy to get your edge computer vision project up and running!

It's Time to Focus on Computer Vision

At the dawn of a new decade, mobile devices already dominate our personal and professional lives. During the 2020s, computer vision (CV) will come more into focus.

Getting Started with the Jetson Nano using alwaysAI

The Jetson Nano is a powerful compactly-packaged AI accelerator that allows you to run intensive models (such as the ones typically used for semantic segmentation and pose estimation) with shorter inference time, while meeting key performance requirements. The Jetson Nano also allows you to speed up lighter models, like those used for object detection, to the tune of 10-25 fps.

alwaysAI Technical Requirements Overview

The developer platform at alwaysAI fast-tracks the creation and deployment of computer vision applications on edge devices, making it easy for you to get started building applications in the field of computer vision.

How to Create and Run a Real-time Object Detector Starter App in Minutes

In this tutorial, we will show you the steps needed to get a real-time object detector starter app up and running quickly and easily on an edge device. You should have already set up your development computer and installed the alwaysAI CLI. For more information on system requirements and supported boards, check out our Docs.

Installing the alwaysAI Platform on Raspberry Pi

Note: The method described in this article is not your only option for installing the base operating system on your Raspberry Pi. You can also use NOOBS, an operating system installation manager to install the base Raspbian operating system. For information on NOOBS go to this website https://www.raspberrypi.org/documentation/installation/noobs.md . If you do use NOOBS to do the initial operating system installation once finished go to Setting Up A Docker Container section of this document...