  We propose a method for learning from streaming visual data using a compact,
constant size representation of all the data that was seen until a given
moment. Specifically, we construct a 'coreset' representation of streaming data
using a parallelized algorithm, which is an approximation of a set with
relation to the squared distances between this set and all other points in its
ambient space. We learn an adaptive object appearance model from the coreset
tree in constant time and logarithmic space and use it for object tracking by
detection. Our method obtains excellent results for object tracking on three
standard datasets over more than 100 videos. The ability to summarize data
efficiently makes our method ideally suited for tracking in long videos in
presence of space and time constraints. We demonstrate this ability by
outperforming a variety of algorithms on the TLD dataset with 2685 frames on
average. This coreset based learning approach can be applied for both real-time
learning of small, varied data and fast learning of big data.

Computer Vision and Pattern Recognition;Machine Learning