Assisted Video Object Tracking

In just three years, a million minutes of video content will cross global IP networks every second. That’s over 100 billion minutes a day. And if that sounds staggering, well, that’s because it is. The thing is, according to Gartner, 99% of this video content is going to be analyzed by machines, not humans. Understanding this video, however, requires smart models and smart models require high-quality training data. That’s why we’re thrilled to announce the newest solution on Figure Eight’s human-in-the-loop platform: Machine Learning assisted Video Object Tracking.

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Build ground truth video datasets for object detection and tracking frame by frame in a sequence of images.

What difference does a tool make?

The traditional approach to an object tracking project is to split the video into individual images and then annotate each image separately, paying careful attention to ensure consistent identifiers for each unique object in sequential images. It's very challenging work, as any Samasource agent or quality analyst will tell you. It takes careful attention to detail and often exceeds the capabilities of most annotation services. (We had to build some supporting tools in our Sama Hub platform to make it tractable.)

Video annotation for object tracking completely changes the game. Now, an entire video sequence can be assessed as a whole, whether the clip contains 2 frames or 2,000 frames. This feature makes it much easier and faster to follow a single object -- even if it's moving -- from beginning to end of a video. If the object disappears from the camera view and reenters later (think: overtaking a cyclist in traffic, only to have them blow past you at the next intersection), we can easily, accurately accommodate it. The whole process is more efficient while maintaining the highest annotation quality, especially as the density of objects increases. And believe me, image complexity at the cutting edge of computer vision is getting up there.

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