As companies welcome more autonomous robots and other heavy equipment into the workplace, we need to ensure equipment can operate safely around human teammates. In this post, we will show you how to build a virtual boundary with computer vision and AWS DeepLens, the AWS deep learning-enabled video camera designed for developers to learn machine learning (ML). Using the machine learning techniques in this post, you can build virtual boundaries for restricted areas that automatically shut down equipment or sound an alert when humans come close. For this project, you will train a custom object detection model with Amazon SageMaker and deploy the model to an AWS DeepLens device. Object detection is an ML algorithm that takes an image as input and identifies objects and their location within the image.

According to the 2018 National Highway Traffic Safety Administration (NHTSA) Traffic Safety Facts, in 2018, there were 857 fatal bicycle and motor vehicle crashes and an additional estimated 47,000 cycling injuries in the US . While motorists often accuse cyclists of being the cause of bike-car accidents, the analysis shows that this is not the case. The most common type of crash involved a motorist entering an intersection controlled by a stop sign or red light and either failing to stop properly or proceeding before it was safe to do so. The second most common crash type involved a motorist overtaking a cyclist unsafely. In fact, cyclists are the cause of less than 10% of bike-car accidents.

New by Hal Rose What you'll learn Introduction to the AWS DeepLens device and associated AWS services Brief introduction to Artificial Intelligence Interest in learning about Machine Learning Description After completing this course, you will be able to discuss A I and Machine Learning with other developers. I'll be referring you to available training material which is available when you are ready to dig deeper. We'll look at the 2019 version of Deep Lens and its amazing structure. We'll go through the unboxing of the device from Amazon and you will be able to quickly register and deploy one of the sample projects in just a few hours. After we have gone through some of the sample projects we'll discuss, and you will understand some of the related Amazon Web Services that are available to be used with DeepLens.