Tracking-based semi-supervised learning
By exploiting tracking information in a semi-supervised learning framework, a classifier trained with three hand-labeled training tracks can produce accuracy comparable to the fully-supervised equivalent (i.e. trained with many thousands of training tracks). This video shows laser track classifications projected into video for easy visualization.
Gray outlines show objects that were tracked in the laser and classified as neither person, bike, nor car.
I am highly excited about this result. Despite the ease with which humans can recognize objects, training a computer to do the same is extremely difficult, and when it is possible, it nearly always requires very large training sets. This requirement makes it impractical for regular people to, for example, train their smarthome to recognize their dog or train their automated farming equipment to recognize a particular type of weed in their fields. Using the techniques of this work, we can lift this requirement.
This is not at all to say the problem is entirely solved. There are many apparent failures in this video, but they are almost entirely due to segmentation and tracking failures. The missing piece now is robust segmentation and tracking.
See the paper for more details.
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