Discriminative poses for early recognition in multi-camera networks

We present a framework for early action recognition in a multi-camera network. Our approach balances recognition accuracy with speed by dynamically selecting the best camera for classification. We follow an iterative clustering approach to learn sets of keyposes that are discriminative for recognition as well as for predicting the best camera for classification of future frames. Experiments on multi-camera datasets demonstrate the applicability of our view-shifting framework to the problem of early recognition.