Learning to recognize visual dynamic events from examples

This paper describes a trainable and flexible system able to recognize visual dynamic events, e.g. movements performed by different people, from a stream of images taken by a fixed camera. Each event is represented by a feature vector built from the spatio-temporal changes detected in the observed image sequence. The system neither attempts to recover the 3D structure nor assumes a prior model of the observed dynamic events. During training a supervisor identifies and labels the events of interest among those automatically detected by the system. At run time, previously unseen events are detected and classified on the basis of the available examples. Several experiments on real images are reported and the benefits of using Support Vector Machines for performing effective classification from a relatively small number of labeled examples and for building noise tolerant representations are discussed. Preliminary results indicate that the proposed system can also be applied with equally good results to the case in which the dynamic events are gestures performed by different people.