Results 31 to 40 of about 458,032 (263)
The Recognition of Action Idea EEG with Deep Learning
Complexity, 2022 The recognition in electroencephalogram (EEG) of action idea is to identify what action people want to do by EEG. The significance of this project is to help people who have trouble in movement.
Guoxia Zou
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Unsafe action recognition in underground coal mine based on cross-attention mechanism
Gong-kuang zidonghua, 2022 The real-time video monitoring and alarming of unsafe actions of coal mine personnel is an important means to improve the level of safety in production. The coal mine underground environment is complex, and the monitoring video quality is poor.
RAO Tianrong, PAN Tao, XU Huijun
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Feature seeding for action recognition [PDF]
2011 International Conference on Computer Vision, 2011 Progress in action recognition has been in large part due to advances in the features that drive learning-based methods. However, the relative sparsity of training data and the risk of overfitting have made it difficult to directly search for good features. In this paper we suggest using synthetic data to search for robust features that can more easily
Pyry Matikainen +2 more
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Action Recognition From Thermal Videos
IEEE Access, 2019 Human action recognition using a camera-based surveillance system remains a challenging task. In particular, action recognition is difficult when a human is not visible in an image captured in a dark environment.
Ganbayar Batchuluun +4 more
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Statistical Machine Learning for Human Behaviour Analysis
Entropy, 2020 Human behaviour analysis has introduced several challenges in various fields, such as applied information theory, affective computing, robotics, biometrics and pattern recognition [...]
Thomas B. Moeslund +4 more
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Deep Learning based Human Action Recognition [PDF]
ITM Web of Conferences, 2021 Human action recognition has become an important research area in the fields of computer vision, image processing, and human-machine or human-object interaction due to its large number of real time applications.
Pandey Ritik +3 more
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Data Mining for Action Recognition [PDF]
, 2015 In recent years, dense trajectories have shown to be an efficient representation for action recognition and have achieved state-of-the-art results on a variety of increasingly difficult datasets. However, while the features have greatly improved the recognition scores, the training process and machine learning used hasn’t in general deviated from the ...
Andrew Gilbert, Richard Bowden
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Human Action Recognition Method Based on Action-Time Perception [PDF]
Jisuanji gongchengTo address the problem of redundant information in action videos and the sparse distribution of feature channels in action information, a 3D residual network based on action-time perception is proposed.
WANG Xiaolu, WEN Jianrong
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Cross-Channel Graph Convolutional Networks for Skeleton-Based Action Recognition
IEEE Access, 2021 In recent years, skeleton-based action recognition, graph convolutional networks, have achieved remarkable performance. In these existing works, the features of all nodes in the neighbor set are aggregated into the updated features of the root node ...
Jun Xie +8 more
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Compressed video ensemble based pseudo-labeling for semi-supervised action recognition
Machine Learning with Applications, 2022 Some recent studies have focused on deep learning based semi-supervised learning for action recognition. However, it is difficult to scale up their training because their input is RGB frames, the obtainment of which incurs computational and storage costs.
Hayato Terao +3 more
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