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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2021

12.10.2020 | Original Article

Spatiotemporal attention enhanced features fusion network for action recognition

verfasst von: Danfeng Zhuang, Min Jiang, Jun Kong, Tianshan Liu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2021

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Abstract

In recent years, action recognition has become a popular and challenging task in computer vision. Nowadays, two-stream networks with appearance stream and motion stream can make judgment jointly and get excellent action classification results. But many of these networks fused the features or scores simply, and the characteristics in different streams were not utilized effectively. Meanwhile, the spatial context and temporal information were not fully utilized and processed in some networks. In this paper, a novel three-stream network spatiotemporal attention enhanced features fusion network for action recognition is proposed. Firstly, features fusion stream which includes multi-level features fusion blocks, is designed to train the two streams jointly and complement the two-stream network. Secondly, we model the channel features obtained by spatial context to enhance the ability to extract useful spatial semantic features at different levels. Thirdly, a temporal attention module which can model the temporal information makes the extracted temporal features more representative. A large number of experiments are performed on UCF101 dataset and HMDB51 dataset, which verify the effectiveness of our proposed network for action recognition.
Vorheriger Artikel Laplacian regularized low-rank sparse representation transfer learning
Nächster Artikel A knowledge discovery and visualisation method for unearthing emotional states from physiological data

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Metadaten
Titel
Spatiotemporal attention enhanced features fusion network for action recognition
verfasst von
Danfeng Zhuang
Min Jiang
Jun Kong
Tianshan Liu
Publikationsdatum
12.10.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2021
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01204-5

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