Res3ATN -- Deep 3D Residual Attention Network for Hand Gesture Recognition in Videos
This work addresses hand gesture recognition, a challenging task in video analysis, but appears incremental as it builds on existing 3D and attention methods.
The paper tackled hand gesture recognition in videos by proposing a 3D residual attention network (Res3ATN), which outperformed existing networks like C3D and ResNet-10 with better accuracy.
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 built 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. We also study and evaluate our baseline network with different number of attention blocks. The comparison shows that the 3D residual attention network with 3 attention blocks is robust in attention learning and is able to classify the gestures with better accuracy, thus outperforming existing networks.