Res3ATN – Deep 3D Residual Attention Network for Hand Gesture Recognition in Videos

Abstract

Hand gesture recognition is a strenuous task to solve in videos. In this paper, we use a 3D residual attention Network which is trained end to end for hand gesture recognition. Based on the stacked multiple attention blocks, we build a 3D network which generates different features at each attention block. Our 3D attention based residual Network (Res3ATN) can be build and extended to very Deep layers. Using this network, an extensive analysis is performed on other 3D networks based on three publicly available datasets. The Res3ATN network performance is compared to C3D, ResNet-10, and ResNext-101 networks. The comparison shows that the 3D residual attention network is robust and is able to learn and classify the gestures with a better accuracy, thus outperforming existing networks.