Journal Article10.1016/J.PATREC.2017.12.003
Large-scale gesture recognition with a fusion of RGB-D data based on optical flow and the C3D model
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TL;DR: An effective 3D Convolutional Neural Network based method for large-scale gesture recognition using RGB-D video data that achieves 54.50% accuracy on the validation subset and 60.93% on the testing subset of the Chalearn LAP IsoGD dataset, both of which outperform the proposed method's results.
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About: This article is published in Pattern Recognition Letters. The article was published on 05 Dec 2017. The article focuses on the topics: Gesture recognition & Convolutional neural network.
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Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition
TL;DR: The proposed method includes two key components: enhanced temporal representation via the proposed 3D Central Difference Convolution (3D-CDC) family, which is able to capture rich temporal context via aggregating temporal difference information; and optimized backbones for multi-sampling-rate branches and lateral connections among varied modalities.
Fusion of 2D CNN and 3D DenseNet for Dynamic Gesture Recognition
TL;DR: An effective dynamic gesture recognition method is proposed by fusing the prediction results of a two-dimensional motion representation convolution neural network (CNN) model and three-dimensional dense convolutional network (DenseNet) model.
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Human Motion Gesture Recognition Algorithm in Video Based on Convolutional Neural Features of Training Images
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Sign Language Recognition Based on R(2+1)D With Spatial–Temporal–Channel Attention
TL;DR: A lightweight spatial–temporal–channel attention module was proposed to make the network concentrate on the significant information along spatial, temporal, and channel dimensions by combining squeeze and excitation attention with self-attention and superior or comparable results to the state-of-the-art methods were obtained.
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Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning
Yunfang Xie,Su Zhang,Yingdi Liu +2 more
TL;DR: Experimental results show that the proposed deep learning algorithm is 5% more accurate than the benchmark three-dimensional CNN (C3D), making it an effective tool to recognize abnormal behaviors of college students in class.
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