Abstract-In this paper we propose a novel method aiming at view-independent multi-view action recognition. Instead of combining the information provided by all the cameras forming the camera setup, for action representation and classification, we perform single-view action representation and classification to all the available videos depicting the person under consideration independently. Action representation involves a self organizing neural network training followed by fuzzy vector quantization. Action classification is performed by a feedforward neural network which is trained for view-invariant action recognition. Multiple action classification results combination based on Bayesian learning, in the recognition phase, results to high action recognition accuracy. The performance of the proposed action recognition method is evaluated on two publicly available databases, aiming at different application scenarios.
Iosifidis et al. REF performed single-view action representation and classification.
Neural representation and learning for multi-view human action recognition
The 2012 International Joint Conference on Neural Networks (IJCNN)
The 2012 International Joint Conference on Neural Networks (IJCNN)
Computer Science