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Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
TL;DR: A compact, effective yet simple method to encode spatio-temporal information carried in 3D skeleton sequences into multiple 2D images, referred to as Joint Trajectory Maps (JTM), and ConvNets are adopted to exploit the discriminative features for real-time human action recognition.
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Abstract: Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image-based recognition. How to effectively use ConvNets for video-based recognition is still an open problem. In this paper, we propose a compact, effective yet simple method to encode spatio-temporal information carried in $3D$ skeleton sequences into multiple $2D$ images, referred to as Joint Trajectory Maps (JTM), and ConvNets are adopted to exploit the discriminative features for real-time human action recognition. The proposed method has been evaluated on three public benchmarks, i.e., MSRC-12 Kinect gesture dataset (MSRC-12), G3D dataset and UTD multimodal human action dataset (UTD-MHAD) and achieved the state-of-the-art results.
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Citations
Enhanced skeleton visualization for view invariant human action recognition
Mengyuan Liu,Hong Liu,Chen Chen +2 more
TL;DR: 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.
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A New Representation of Skeleton Sequences for 3D Action Recognition
Qiuhong Ke,Mohammed Bennamoun,Senjian An,Ferdous Sohel,Farid Boussaid +4 more
- 01 Jul 2017
TL;DR: Wang et al. as mentioned in this paper proposed to use deep convolutional neural networks to learn long-term temporal information of the skeleton sequence from the frames of the generated clips, and then use a Multi-Task Learning Network (MTLN) to jointly process all frames in parallel to incorporate spatial structural information for action recognition.
A New Representation of Skeleton Sequences for 3D Action Recognition
TL;DR: Deep convolutional neural networks are proposed to be used to learn long-term temporal information of the skeleton sequence from the frames of the generated clips, and a Multi-Task Learning Network (MTLN) is proposed to jointly process all Frames of the clips in parallel to incorporate spatial structural information for action recognition.
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Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks
TL;DR: Wang et al. as discussed by the authors proposed a global context-aware attention LSTM for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using global context memory cell.
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Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition
TL;DR: A deep learning-based approach for temporal 3D pose recognition problems based on a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) recurrent network and a data augmentation method that has also been validated experimentally is proposed.
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