Large-scale gesture recognition with a fusion of RGB-D data based on the C3D model

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RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, methodology): “[79] adopted C3D [130] to extract features separately from RGB and depth modalities, and used the concatenated for SVM classifier.”
Large-scale Isolated Gesture Recognition using Convolutional Neural Networks (2016, methodology): “Rank Team Recognition rater 1 FLiXT [ 38] 56.8968% 2 AMRL (ours) 55.5733% 3 XDETVP-TRIMPS [39] 50.9329% 4 ICT NHCI 46.8027% 5 XJTUfx 43.9164% 6 TARDIS 40.1531% 7 NTUST 20.3317% sequences into images.”
Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks (2018, methodology): “C3D [56] applied 3D convolutional networks to both depth and RGB channels and fused TABLE III COMPARATIVE ACCURACY OF PROPOSED METHOD AND BASELINE METHODS ON THE CHALEARN LAP ISOGD DATASET”
Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM (2017, methodology): “[12] trained their models based on the pre-trained model on the large-scale Sports-1M dataset [36], but the proposed networks are only trained on the IsoGD dataset.”