Many of us spend most of our days hunched over a desk, leaning forward looking at a computer screen, or slumped down in our chair. If you’re like me, you’re only reminded of your bad posture when your neck or shoulders hurt hours later, or you have a splitting migraine. Wouldn’t it be great if someone could remind you to sit up straight? The good news is, you can remind yourself! In this tutorial, we’ll build a posture corrector app using a pose estimation model available from alwaysAI.
Separating certain variables from the main application into a configuration file can improve your development time by reducing the need to recompile apps for minor changes. In this tutorial, we’ll cover how to setup a very basic method for leveraging a JSON file for runtime configuration options.
Many models, including those for pose estimation, may have much better performance when run on a GPU rather than a CPU. In this tutorial, we’ll cover how to run pose estimation on the Jetson Nano B01 and cover some nuances of running starter apps on this edge device.
Building and running your app on alwaysAI can be done a few different ways, depending on the platform you want to develop on and the device you want to deploy on. We’ve concentrated these options in one place for your convenience and we’ll update this document as the platform evolves!
alwaysAI’s Co-Founder & CEO, Marty Beard, recently shared in a quarterly report (click here or see video below) key updates around the company’s product, developer, partner and corporate progress - as well as exciting news for the remaining months of 2020.
alwaysAI set out to create a platform for developers to easily and affordably build and deploy computer vision (CV) applications on edge devices. Early this year, alwaysAI came out of beta and officially released a...
Training a computer vision model is one component of a complex and iterative undertaking, which can often seem daunting. At alwaysAI we want to make the process simple and approachable. To get you started, we have compiled a general overview of the training process of Deep Neural Networks (DNNs) for use in computer vision applications. We will focus on supervised learning in this overview, which uses labeled training data to teach the model what the desired output is. This article provides...
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.
The process of developing computer vision applications has been greatly simplified by alwaysAI, which now includes native support for Mac OSX (Mojave and Catalina), and enables developers to get started prototyping applications right away with very little setup required.
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...
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...
In this guide, we’ll make an app that can count vehicles in real-time utilizing object counting, a technique used in computer vision that combines object detection and object tracking. Our final computer vision application will tell us how many objects of a specific kind are currently being detected in a video stream.
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...
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.
If you have a host of images that you’d like to sort based on the presence of particular things (like people, cars, buildings, etc.), using computer vision classifiers can make this a pretty simple and fast thing to accomplish.
During the 2020s, artificial intelligence (AI), robotics and machine learning will transform the marine industry. New developments with autonomous underwater vehicles will require processing huge amounts of data, propelling AI advancements. Seafloor Systems, Inc. is diving right in.
Sockets are endpoints for inter-process communication over the network, which is supported by most platforms. Using sockets with the alwaysAI platform allows an application to communicate with external applications running locally or externally, as well as with applications written in different programming languages. There are many methods for inter-process communication, but cross-platform communication is handled best by sockets.
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...
In this guide, we’ll be focusing on image classification. What is image classification? It is a technique used in computer vision to identify and categorize the main content in a photo or video.
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.
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!
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.
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.
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.
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.
With the new alwaysAI model catalog, you can now search for a computer vision model by specific criteria; for instance, you can search by label to ensure that the model includes a label for the type of entity that your application needs to be able to recognize. You can also search for a model that is compatible with the kind of service that you want your application to provide (object detection, image classification, or pose estimation), and then further refine your search with filters for...
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...
The AI & Big Data Expo was a great success for alwaysAI. We were a sponsor at the event, which took place at the Santa Clara Convention Center on November 13th & 14th. Our booth was met with an overwhelmingly positive response as we demonstrated how to easily create and deploy computer vision apps on the edge with the alwaysAI platform, and attendees got to witness these applications performing in real time.
CEO Marty Beard gave a compelling talk at the Convergent Technology Stage,...
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.
alwaysAI, a developer platform that fast-tracks the creation and deployment of computer vision apps on edge devices, today announced significant momentum following the close of its Seed funding earlier this year. In addition to adding more than 500 developers to its beta program and expanding its corporate team, alwaysAI also formed two new partnerships for rapid computer vision prototype development and deepened its relationships with NVIDIA and Qualcomm Technologies, Inc.
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.
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.
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. We have more information on hardware accelerators in our documentation, which is available now.
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...
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.
Want to speak with us about opportunities for your business to accelerate Deep Learning Computer Vision on the edge? Interested in a demo of the alwaysAI platform? Our team will be at AI events in September, October and November 2019 in California, and we're open for conversations.
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...
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...
It's time: software and hardware technologies are advancing to a place where you can easily equip low-power, resource-constrained edge devices with AI computer vision capabilities. Here's a brief overview of how CV started, what CV is now, where it might be going, and how you can use it to empower your existing equipment or new project. Ultimately, developers everywhere are now able to easily build and deploy deep learning computer vision applications to make your business more functional,...
alwaysAI deepened its management bench today with the appointment of Scott Miller as Head of Product and Partner Management.
alwaysAI provides a platform to deploy computer-vision applications onto edge devices.Learn More