Human action recognition is challenging, due to large temporal and spatial variations in actions performed by humans. These variations include significant nonlinear temporal stretching. In this paper, we propose an intuitively simple method to extract action templates from 3D human joint data that is insensitive to nonlinear stretching. The extracted action templates are used as the training instances of the actions to train multiple classifiers including a multi-class SVM classifier. Given an unknown action, we first extract and classify all its constituent atomic actions and then assign the action label via an equal voting scheme. We have tested the method on two public datasets that contain 3D human skeleton data. The experimental results show the proposed method can obtain a comparable or better performance than published state-ofthe-art methods. Additional experiments also demonstrate the method works robustly on randomly stretched actions.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.