Open Data Cam Combines Camera, GPU, and Neural Network in an Artisanal DIY Cereal Box

The engineers and product designers at [moovel lab] have created the Open Data Cam – an AI camera platform that can identify and count objects as they move through its field of view – along with an open source guide for making your own.

Step one: get out your ruler and utility knife. In this world of ubiquitous 3D-printers they’ve taken a decidedly low-tech approach to the project’s enclosure: a cut, folded, and zip-tied plastic box, with a cardboard frame inside to hold the electronic bits. It’s “splash proof” and certainly cheap to make, but we’re a little worried about cooling and physical protection for the electronics inside, as they’re not exactly cheap and rugged components.

So what’s inside? An Nvidia Jetson TX2 board, a LiPo battery with some charging circuitry, and a standard webcam. The special sauce, however, is the software, which is available on GitHub. [Moovel lab]’s engineers have put together a nice-looking wifi-accessible mobile UI for marking the areas where you’d like the software to identify and tally objects. The actual object detection and identification tasks are performed by the speedy YOLO neural network, a task the Nvidia board’s GPU is of course well suited for.

As the Open Data Cam’s unblinking glass eye gazes upon our urban environments, it will log its observations in an ancient and mysterious language: CSV. It’s up to you, human, to interpret this information and use it for good.

A summary video and build time lapse are embedded after the break.

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Shoot Video in 26 Different Directions

[Mark Mullins] is working on a project called Quamera: a camera that takes video in every direction simultaneously, creating realtime 3D environments on the fly.

[Mark] is using 26 Arducams, arranging them in a rhombicuboctahedron configuration, which consists of three rings of 8 cameras with each ring controlled by a Beaglebone; the top and bottom rings are angled at 45 degrees, while the center ring looks straight out. The top and bottom cameras are controlled by a fourth Beaglebone, which also serves to communicate with the Nvidia Jetson TX1 that runs everything. Together, these cameras can see in all directions at once, with enough overlap for provide a seamless display for viewers.

In the image to the right, [Mark] is testing out his software for getting the various cameras to work together. The banks of circles and the dots and lines connecting to them represent the computer’s best guess on how to seamlessly merge the images.

If you want to check out the project in person, [Mark] will be showing off the Quamera at the Dover Mini Maker Faire this August. In the meantime, to learn more about the Jetson check out our thorough overview of the board.

Hands-On Nvidia Jetson TX2: Fast Processing for Embedded Devices

The review embargo is finally over and we can share what we found in the Nvidia Jetson TX2. It’s fast. It’s very fast. While the intended use for the TX2 may be a bit niche for someone building one-off prototypes, there’s a lot of promise here for some very interesting applications.

Last week, Nvidia announced the Jetson TX2, a high-performance single board computer designed to be the brains of self-driving cars, selfie-snapping drones, Alexa-like bots for the privacy-minded, and other applications that require a lot of processing on a significant power budget.

This is the follow-up to the Nvidia Jetson TX1. Since the release of the TX1, Nvidia has made some great strides. Now we have Pascal GPUs, and there’s never been a better time to buy a graphics card. Deep learning is a hot topic that every new CS grad wants to get into, and that means racks filled with GPUs and CUDA cores. The Jetson TX1 and TX2 are Nvidia’s strike at embedded deep learningor devices that need a lot of processing power without sucking batteries dry.

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