Results 31 to 40 of about 440,945 (164)
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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International Journal For Multidisciplinary Research, 2023 human-to-human interaction and interpersonal relations Human activity recognition plays a significant role. Because to identity of a person, their personality, and psychological state it provides information, it is difficult to extract. another person's activities is one of the main subjects of study of the scientific areas of computer vision The human
Shraddha Marotkar -, A.B. Kharate -
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Lightweight Semantic-Guided Neural Networks Based on Single Head Attention for Action Recognition
Sensors, 2022 Skeleton-based action recognition can achieve a relatively high performance by transforming the human skeleton structure in an image into a graph and applying action recognition based on structural changes in the body.
Seon-Bin Kim +3 more
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Efficient Action Recognition with MoFREAK [PDF]
2013 International Conference on Computer and Robot Vision, 2013 Recent work shows that local binary feature descriptors are effective for increasing the efficiency of object recognition, while retaining comparable performance to other state of the art descriptors. An extension of these approaches to action recognition in videos would facilitate huge gains in efficiency, due to the computational advantage of ...
Chris Whiten +2 more
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Short-Term Action Learning for Video Action Recognition
IEEE AccessFor a long-term complex Action, it is typically composed of various short-term Actions. The speed and importance of these short-term Actions directly affect the recognition results.
Liu Ting-Long
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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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Research on Human Motion Recognition Based on Data Redundancy Technology
Complexity, 2021 Aiming at the problems of low recognition rate and slow recognition speed of traditional body action recognition methods, a human action recognition method based on data deduplication technology is proposed.
Hong-Lan Yang +2 more
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Meta‐action descriptor for action recognition in RGBD video
IET Computer Vision, 2017 Action recognition is one of the hottest research topics in computer vision. Recent methods represent actions based on global or local video features. These approaches, however, lack semantic structure and may not provide a deep insight into the essence ...
Min Huang +5 more
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Rank Pooling for Action Recognition [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 IEEE Transactions on Pattern Analysis and Machine ...
Basura Fernando +4 more
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Kernelized covariance for action recognition [PDF]
2016 23rd International Conference on Pattern Recognition (ICPR), 2016 In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only. We present a rigorous and principled mathematical pipeline to recover the kernel trick for computing the covariance matrix, enhancing it to model more complex, non-linear relationships conveyed by ...
Cavazza, Jacopo +3 more
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