Journal ArticleDOI
Enhanced skeleton visualization for view invariant human action recognition
Mengyuan Liu,Hong Liu,Chen Chen +2 more
TLDR
Enhanced skeleton visualization method encodes spatio-temporal skeletons as visual and motion enhanced color images in a compact yet distinctive manner and consistently achieves the highest accuracies on four datasets, including the largest and most challenging NTU RGB+D dataset for skeleton-based action recognition.About:Â
This article is published in Pattern Recognition.The article was published on 2017-08-01. It has received 668 citations till now. The article focuses on the topics: RGB color model & Convolutional neural network.read more
Citations
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Proceedings ArticleDOI
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
TL;DR: Zhang et al. as mentioned in this paper proposed a two-stream adaptive graph convolutional network (2s-AGCN) to model both the first-order and the second-order information simultaneously, which shows notable improvement for the recognition accuracy.
Journal ArticleDOI
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
TL;DR: This work introduces a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames, and investigates a novel one-shot 3D activity recognition problem on this dataset.
Proceedings ArticleDOI
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
TL;DR: The proposed AS-GCN achieves consistently large improvement compared to the state-of-the-art methods and shows promising results for future pose prediction.
Proceedings ArticleDOI
Skeleton-Based Action Recognition With Directed Graph Neural Networks
TL;DR: A novel directed graph neural network is designed specially to extract the information of joints, bones and their relations and make prediction based on the extracted features and is tested on two large-scale datasets, NTU-RGBD and Skeleton-Kinetics, and exceeds state-of-the-art performance on both of them.
Proceedings ArticleDOI
Skeleton-Based Action Recognition With Shift Graph Convolutional Network
TL;DR: The proposed Shift-GCN notably exceeds the state-of-the-art methods with more than 10 times less computational complexity, and is composed of novel shift graph operations and lightweight point-wise convolutions.
References
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ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Image Analysis and Mathematical Morphology
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Proceedings Article
Two-Stream Convolutional Networks for Action Recognition in Videos
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work proposes a two-stream ConvNet architecture which incorporates spatial and temporal networks and demonstrates that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data.
Proceedings ArticleDOI
Real-time human pose recognition in parts from single depth images
Jamie Shotton,Andrew Fitzgibbon,Mat Cook,Toby Sharp,Mark J. Finocchio,Richard E. Moore,Alex Aben-Athar Kipman,Andrew Blake +7 more
TL;DR: This work takes an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem, and generates confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.
Proceedings ArticleDOI
Return of the Devil in the Details: Delving Deep into Convolutional Nets
TL;DR: It is shown that the data augmentation techniques commonly applied to CNN-based methods can also be applied to shallow methods, and result in an analogous performance boost, and it is identified that the dimensionality of the CNN output layer can be reduced significantly without having an adverse effect on performance.
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