MultiD-CNN: A multi-dimensional feature learning approach based on deep convolutional networks for gesture recognition in RGB-D image sequences

作者：

Highlights：

• We propose a multi-dimensional deep learning-based method for gesture recognition.

• Learning spatiotemporal features from RGB-D videos and their motion representation.

• We investigate different fusion strategies to boost the recognition performance.

• We show efficiency to other video classification tasks (i.e. activity recognition).

• The proposed method achieves the state-of-the-art results on several datasets.

摘要

•We propose a multi-dimensional deep learning-based method for gesture recognition.•Learning spatiotemporal features from RGB-D videos and their motion representation.•We investigate different fusion strategies to boost the recognition performance.•We show efficiency to other video classification tasks (i.e. activity recognition).•The proposed method achieves the state-of-the-art results on several datasets.

论文关键词：Gesture recognition,Deep learning,Convolutional neural networks,Multimodal learning,Feature fusion,RGB-D video processing

论文评审过程：Received 19 October 2018, Revised 10 June 2019, Accepted 19 July 2019, Available online 20 July 2019, Version of Record 30 July 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112829