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.
Easy-to-use development platform brings together pre-trained computer vision models, innovative APIs, starter applications and edge environments
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.
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 Series A 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.
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.
Two experienced tech executives moved to San Diego to create a company that would make it easier for developers to use AI technologies. Former Blackberry COO Marty Beard and former Sphera Solutions CTO Steve Griset founded AlwaysAI in Solana Beach last May, picking the San Diego area for its expertise in big data, neuroscience and mobile technology.
“The platform’s tools will allow for the development of computer vision systems that can detect, classify, count, and track objects, and that can perform facial recognition.”
alwaysAI today announced the close of a $4M Series A funding round to bring its deep learning computer vision (CV) platform for embedded devices to enterprise developers. The funding was led by BRV, a leading Silicon Valley venture capital firm, with participation from co-founder and CEO Marty Beard. alwaysAI will use its funding to broaden and commercialize its deep learning CV platform and expand its team.
The San Diego-based artificial intelligence startup alwaysAI has announced a $4 million Series A funding round backed by BlueRun Ventures. The company announced that this latest investment will bring its deep learning computer vision (CV) platform to developers.
Many of our devices, from wearables to medical instruments to drones, can capture reams of images. Using algorithms to analyze those images on the devices, in turn, generally takes loads of computing power and energy—sometimes more than is feasible.
alwaysAI provides a platform to deploy computer-vision applications onto edge devices.Learn More