Multi-view human action recognition under occlusion based on Fuzzy distances and neural networks

While action recognition methods exploiting information coming from multiple viewing angles have been proposed in order to overcome the known viewing angle assumption of single-view methods, they set the assumption that the person under consideration is visible from all the cameras forming the adopted camera setup. However, this assumption is not usually met in real applications and, thus, their applicability is limited. In this paper we propose a novel action recognition method that overcomes this assumption. The method exploits information coming from an arbitrary number of viewing angles. The classification procedure involves Fuzzy Vector Quantization and Artificial Neural Networks. Experiments on two publicly available action recognition databases evaluate the effectiveness of the proposed action recognition approach.