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Marius Muja from University of British Columbia returned to Willow
Garage this summer to continue his work object recognition. In
addition to working on an object detector that can scale to a large
number of objects, he has also been designing a general object
recognition infrastructure.
One problem that many object detectors have is that they get slower as
they learn new objects. Ideally we want a robot that goes into an
environment and is capable of collecting data and learning new objects
by itself. In doing this, however, we don't want the robot to get
progressively slower as it learns new objects.
Marius worked on an object detector called Binarized Gradient Grid
Pyramid (BiGGPy), which uses the gradient information from an image to
match it to a set of learned object templates. The templates are
organized into a template pyramid. This tree structure has low
resolution templates at the root and higher resolution templates at
each lower level. During detection, only a fraction of this tree must
be explored. This results in big speedups and allows the detector to
scale to a large number of objects.
