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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. 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.

See the paper for more details.

Projection of laser data into external camera

This video shows distance readings from a Velodyne HDL-64E projected into a camera on top of Hoover Tower at Stanford. Points are colored by distance to the car.

Combining this system with object recognition from track classification and tracking-based semi-supervised learning, it is possible to produce very large datasets of aerial views of objects with little human intervention.

If you are interested in using some of this data, please email me.

Sensor overlay

This video shows output from two sensors on Junior - the Ladybug 3 panoramic camera and Velodyne HDL-64E laser range finder. Overlay points are colored by distance; red is close, green is far away.