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.

In this tutorial, we’ll cover how to create your own license plate tracker using the new license plate detection model, which was created using alwaysAI’s model training tool.

In this tutorial, we’ll walk through the steps and decision points involved in the creation of the ‘alwaysai/vehicle_license_mobilenet_ssd’ model, an object detection model for identifying vehicles and license plates. 

This architectural guide walks you through one way of creating an app for monitoring when things (animals, cars, etc) come and go.

Many people are now working or learning from home, introducing the new issue of enforcing professionalism and academic honesty by remote. Whether you are trying to prevent students from cheating on tests, want to double check that your kids aren’t just playing on their phones, or maybe you want to stop yourself from checking social media while you’re supposed to be working, computer vision can help. In this tutorial, I’ll show how you can create your own contraband object alert system using...

There are many use cases in which it could be beneficial to have automated text messages sent that contain data obtained from computer vision. Perhaps you’d like to be notified whenever a person or animal walks into your yard or house (by using object detection), or when your kids appear to be fighting (by using pose estimation), or any other number of scenarios — the possibilities are endless! In this tutorial, we’ll show you how easy it is to accomplish this task by using a very basic...

Watching the Road Less Traveled

productOps is a boutique software development and consulting firm located in Santa Cruz, California. They serve a broad range of clients across a number of industries, creating custom applications and answering general business challenges.

Christian Piper is a 16-year-old high school student from Pennsylvania, who equipped his First Robotics Competition robot with machine learning sight. He did this with the alwaysAI platform.

In this article I will demonstrate how to easily modify existing apps offered with alwaysAI to use two object detection models simultaneously, and to display the output in side-by-side frames.

Imagine how the world would change if we could easily extend the functions of our visual cortex to machines. Computer Vision (CV) has provided us with immense opportunities to build systems and machines that can change the world. We took our CV platform, alwaysAI, to hackathons at top universities in California: University of San Diego, University of Southern California, and Stanford University. With less than 48 hours to complete their projects, students delivered phenomenal projects using...

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...

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.

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.

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...

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.

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.

If you don't have an alwaysAI account yet, you can sign-up here.

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.

Computer vision-based deep learning projects might seem far beyond the kind of what you and your development team have tackled in the past. However, though it is an emerging technology, computer vision application development cycles remain relatively similar to that of many projects you may already be familiar with.

Developers have several libraries available to them to make the process of building and deploying computer vision simpler and more effective. Most of these libraries are written in C/C++, ensuring a fast execution. In most cases, however, there is a Python API that wraps the C++ implementation. This is because Python has become the go-to language for prototyping and developing deep neural networks. An extremely popular and versatile language, Python enables interactive development, and its...

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...

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.

Whenever any collection of objects enter your field of vision, your brain instinctively begins the processes of recognition and localization. One of the core challenges of computer vision is to replicate this intelligence in a computer. This is impossible without a proper understanding of human vision and visual perception. The study of biological vision has revealed that the human eyes and brain are connected to an intricate level of functioning. While the eyes receive the visual data, the...