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...

alwaysAI announces exciting new features

If you haven’t already started thinking about building computer vision into your products, now is the time.

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.

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!

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.

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 to Boost Performance on an Edge Device

In this tutorial, we will show you the steps needed to boost the performance of your edge device. You will need a hardware accelerator that is supported by alwaysAI – such as Intel’s Neural Compute Stick 2. You can read more about supported edge devices and how to set the engine and accelerator using the edgeiq API in our documentation.

Finding Things in an Image in Real Time on the Edge

Recent advances in technology have greatly broadened the scope of object detection and related computer vision (CV) services. Hardware with advanced features paired with smarter neural networks has attracted developers and data scientists from numerous industries to start leveraging computer vision to solve complex business challenges. Combined with the rising popularity of embedded devices capturing data on the edge, computer vision on a grand scale has been exploding with seemingly endless...

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...

Using Object Detection on IoT Devices

Something big is happening. The real world is being radically transformed by tech that has never had as much intelligence and capability as it does today. Sea changes are on the horizon and forward-thinking businesses are shifting their development focus to arguably the most significant change since the invention of the printed circuit board.