Voxel51 raises $2 million for its video-native identification of people, cars and more

Many companies and municipalities are saddled with hundreds or thousands of hours of video and limited ways to turn it into usable data. Voxel51 offers a machine learning-based option that chews through video and labels it, not just with simple image recognition but with an understanding of motions and objects over time.

Annotating video is an important task for a lot of industries, the most well known of which is certainly autonomous driving. But it’s also important in robotics, the service and retail industries, for police encounters (now that body cams are becoming commonplace), and so on.

It’s done in a variety of ways, from humans literally drawing boxes around objects every frame and writing what’s in it to more advanced approaches that automate much of the process, even running in real time. But the general rule with these is that they’re done frame by frame.

A single frame is great if you want to tell how many cars are in an image, or whether there’s a stop sign, or what a license plate reads. But what if you need to tell whether someone is walking or stepping out of the way? What about whether someone is waving or throwing

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