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...

The code for this tutorial is available here: https://github.com/alwaysai/video-streamer

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.

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.

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.

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...

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.

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.

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.