FlickerNet: Adaptive 3D Gesture Recognition from Sparse Point Clouds

Yuecong Min (Institute of Computing Technology, Chinese Academy of Sciences), Xiujuan Chai (Agricultural Information Institute), Lei Zhao (HUAWEI Technologies Co., Ltd.), Xilin Chen (Institute of Computing Technology, Chinese Academy of Sciences)

Abstract
Recent studies on gesture recognition use deep convolutional neural networks (CNNs) to extract spatiotemporal features from individual frames or short video clips. However, extracting features frame-by-frame will bring a lot of redundant and ambiguous gesture information. Inspired by the flicker fusion phenomena, we propose a simple but efficient network, called FlickerNet, to recognize gesture from a sequence of sparse point clouds sampled from depth videos. Different from the existing CNN-based methods, FlickerNet can adaptively recognize hand postures and hand motions from the flicker of gestures: the point clouds of the stable hand postures and the sparse point-cloud motion for fast hand motions. Notably, FlickerNet significantly outperforms the previous state-of-the-art approaches on two challenging datasets with much higher computational efficiency.

DOI
10.5244/C.33.57
https://dx.doi.org/10.5244/C.33.57

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