Here's a cool project that uses a Raspberry Pi and Tensorflow to classify objects for recycling. A Raspberry Pi camera is used to capture an image of the item on the tray. Based on the output of the model, the robot will choose one of three bins and use a couple stepper motors to drop it in. It's surprisingly fast for a single board computer!
There is also an "active training" mode which sends images up to a server for future training data. It looks like there is some sort of UI for manual data annotation. Since training takes quite a bit of computing power, it's no surprise that he chose to do it on a powerful machine in the cloud.
I was curious how he was able to load Tensorflow on the pi, so I did some digging and found a couple cool projects on Github.
Google recently announced their new "video intelligence" API at the Google Next conference in San Francisco on March 7th. The API aims at providing shot-by-shot annotations of objects in videos.
While other object classification APIs already exist, they are mainly geared towards images. This is the first API I have seen that classifies objects in videos.
Possibly applications include video spam filters, automated classification/filtering, annotations for the blind, and shot change detection. I could also see the object detection feature being useful for DIY autonomous vehicles if this API gains the ability to stream and process live video in the future. We will see if we can utilize this tool for something interesting in one of our upcoming videos.