1 research outputs found
Learning detectors quickly using structured covariance matrices
Computer vision is increasingly becoming interested in the rapid estimation
of object detectors. Canonical hard negative mining strategies are slow as they
require multiple passes of the large negative training set. Recent work has
demonstrated that if the distribution of negative examples is assumed to be
stationary, then Linear Discriminant Analysis (LDA) can learn comparable
detectors without ever revisiting the negative set. Even with this insight,
however, the time to learn a single object detector can still be on the order
of tens of seconds on a modern desktop computer. This paper proposes to
leverage the resulting structured covariance matrix to obtain detectors with
identical performance in orders of magnitude less time and memory. We elucidate
an important connection to the correlation filter literature, demonstrating
that these can also be trained without ever revisiting the negative set