Spatiotemporal features learning with 3DPyraNet

Abstract

A discriminative approach based on the 3DPyraNet model for spatiotemporal feature learning is proposed. In combination with a linear SVM classifier, our model outperform state-of-the-art methods on two datasets (KTH, Weizmann). Whereas, shows comparable result with current best methods on third dataset (YUPENN). The features are compact, achieving 94.08 %, 99.13 %, and 94.67% accuracy on KTH, Weizmann, and YUPENN, respectively. The proposed model appears more suitable for spatiotemporal feature learning compared to traditional feature learning techniques; also, the number of parameters is far less than other 3DConvNets.