The alwaysAI Blog

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.

How to Detect Pedestrians and Bicyclists in a Cityscape Video

Detecting pedestrians and bicyclists in a cityscape scene is a crucial part of autonomous driving applications. Autonomous vehicles need to determine how far away pedestrians and bicyclists are, as well as what their intentions are. A simple way to detect people and bicycles is to use Object Detection. However, in this case we need much more detailed information about the exact locations of the pedestrians and bicyclists than Object Detection can provide, so we’ll use a technique called...

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.

How to Detect People Using alwaysAI

Detecting people can be an important part of applications across many industries. Common use cases include security applications that track who’s coming and going, as well as safety systems designed to keep people out of harm’s way.

Using Pose Estimation and Object Detection to Rescue the Elderly

At the AWS re:Invent conference, we deepened our collaboration with Qualcomm® Technologies by demonstrating real-time object detection and pose estimation on the Qualcomm® Robotics RB3 platform. Based on the Qualcomm® SDA845 system-on-a-chip (SoC), the RB3 platform enables the creation of high-performing computer vision applications on robots and other IoT devices. We built an application on a demo robot that showcased the powerful combination of the Qualcomm® Robotics RB3 platform, our deep...

How We Built a Conference Booth Tracking App

alwaysAI offers a number of starter apps that make it easy to quickly deploy computer vision (CV) based applications. In this demo, I'm going to show you how to extend one of these starter apps and, hopefully, provide some insight about how you can create your own custom CV apps. The app we're going to end up with is meant to be used on a conference booth, to track the number of attendees who stop by and provide some basic metrics on how much time they spend at the booth.

Developing with alwaysAI on Windows

The process of developing computer vision applications has been greatly simplified by alwaysAI, which includes native support for both Windows and Linux, and enables developers to get started prototyping applications right away with very little setup required.

How To Change Computer Vision Models in the alwaysAI Platform

In this tutorial, we will show you the steps needed to change the computer vision model in the alwaysAI application. Your development computer and edge device (if you're using one) should be set up based on the instructions on our dashboard. You should also have an app running like this object detector starter app. Finally, you should have a Terminal window open.

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 Ubuntu Desktop

Although alwaysAI is focused on computer vision on the edge, you can easily install the platform on your local PC and do prototyping before going to the edge.  In this article, I will show how to install the alwaysAI platform on your PC using either a virtual machine (macOS, Windows) or native installation (Linux).  If you are already running a Linux desktop you can skip down to the Installing alwaysAI Platform section. 

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 . 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...