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Human gesture recognition has drawn much attention in the area of computervision. However, the performance of gesture recognition is always influenced bysome gesture-irrelevant factors like the background and the clothes ofperformers. Therefore, focusing on the regions of hand/arm is important to thegesture recognition. Meanwhile, a more adaptive architecture-searched networkstructure can also perform better than the block-fixed ones like Resnet sinceit increases the diversity of features in different stages of the networkbetter. In this paper, we propose a regional attention witharchitecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. We replacethe fixed Inception modules with the automatically rebuilt structure throughthe network via Neural Architecture Search (NAS), owing to the different shapeand representation ability of features in the early, middle, and late stage ofthe network. It enables the network to capture different levels of featurerepresentations at different layers more adaptively. Meanwhile, we also designa stackable regional attention module called dynamic-static Attention (DSA),which derives a Gaussian guidance heatmap and dynamic motion map to highlightthe hand/arm regions and the motion information in the spatial and temporaldomains, respectively. Extensive experiments on two recent large-scale RGB-Dgesture datasets validate the effectiveness of the proposed method and show itoutperforms state-of-the-art methods. The codes of our method are available at:https://github.com/zhoubenjia/RAAR3DNet.

Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition