Results 31 to 40 of about 2,738,599 (315)
Temporal Salience Based Human Action Recognition [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 This paper proposes a new approach for human action recognition exploring the temporal salience. We exploit features over the temporal saliency maps for learning the action representation using a local dense descriptor. This approach automatically guides the descriptor towards the most interesting contents, i.e.
Al-Obaidi, S., Abhayaratne, C.
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Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset [PDF]
Computer Vision and Pattern Recognition, 2017 The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks. This paper re-evaluates state-
João Carreira, Andrew Zisserman
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Pairwise Features for Human Action Recognition [PDF]
2010 20th International Conference on Pattern Recognition, 2010 Existing action recognition approaches mainly rely on the discriminative power of individual local descriptors extracted from spatio-temporal interest points (STIP), while the geometric relationships among the local features are ignored. This paper presents new features, called pairwise features (PWF), which encode both the appearance and the spatio ...
Ta, Anh Phuong +4 more
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Spatiotemporal Saliency for Human Action Recognition [PDF]
2005 IEEE International Conference on Multimedia and Expo, 2005 This paper addresses the problem of human action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. We detect the spatiotemporal salient points by measuring changes in the information content of pixel neighborhoods not only ...
Oikonomopoulos, A, Patras, I, Pantic, M
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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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Interior Human Action Recognition Method Based on Prior Knowledge of Scene [PDF]
Jisuanji kexue, 2022 Currently,the recognition technology targeted at human action in an interior scene is widely used in video content understanding,home-based care,medical care and other fields,and existing researches pay more heed to the modelling of human action,while ...
LIU Xin, YUAN Jia-bin, WANG Tian-xing
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Aerial Insights: Deep Learning-Based Human Action Recognition in Drone Imagery
IEEE Access, 2023 Human action recognition is critical because it allows machines to comprehend and interpret human behavior, which has several real-world applications such as video surveillance, robot-human collaboration, sports analysis, and entertainment.
Usman Azmat +6 more
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Distinctive action sketch for human action recognition [PDF]
Signal Processing, 2018 Abstract Recent developments in the field of computer vision have led to a renewed interest in sketch correlated research. There have emerged considerable solid evidence which revealed the significance of sketch. However, there have been few profound discussions on sketch based action analysis so far. In this paper, we propose an approach to discover
Zheng, Ying +4 more
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Video-based Human Action Recognition using Deep Learning: A Review [PDF]
arXiv.org, 2022 Human action recognition is an important application domain in computer vision. Its primary aim is to accurately describe human actions and their interactions from a previously unseen data sequence acquired by sensors.
Hieu Pham +4 more
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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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