Spatio-temporal Feature Recogntion using Randomised Ferns

In this paper we present a generic classifier for detecting spatio-temporal feature points within video. The premise being that given a feature detector, we can learn a classifier that duplicates its functionality which is both accurate and computationally efficient. This means that feature point detection can be achieved independent of the complexity of the original interest point formulation. We extend the naive Bayesian classifier of Ferns to the spatio-temporal domain and learn classifiers that duplicate the functionality of common spatio-temporal interest point detectors. Results demonstrate accurate reproduction of results with a classifier that can be applied exhaustively to video at frame-rate, without optimisation, in a scanning window approach.