Multiple stream deep learning model for human action recognition-Reference-Cited by-同舟云学术

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Multiple stream deep learning model for human action recognition

Published:2020-01 Issue: Volume:93 Page:103818
ISSN:0262-8856
Container-title:Image and Vision Computing
language:en
Short-container-title:Image and Vision Computing

Author:

Gu Ye^ORCID,Ye Xiaofeng,Sheng Weihua,Ou Yongsheng,Li Yongqiang

Funder

National Natural Science Foundation of China

Shenzhen Overseas High Level Talent (Peacock Plan) Program

Shenzhen Basic Research Projects

Publisher

Elsevier BV

Subject

Computer Vision and Pattern Recognition,Signal Processing

Reference44 articles.

1. Going deeper into action recognition: a survey;Herath;CoRR,2016

2. Two-stream convolutional networks for action recognition in videos;Simonyan;CoRR,2014

3. Microsoft kinect sensor and its effect;Zhang;IEEE MultiMed.,2012

4. Motion recognition by higher order local auto correlation features of motion history images;Watanabe,2008

5. Hierarchical filtered motion for action recognition in crowded videos;Tian;IEEE Trans. Syst. Man Cybern. Part C,2012

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1. FTAN: Frame-to-frame temporal alignment network with contrastive learning for few-shot action recognition;Image and Vision Computing;2024-09

2. Spatio‐temporal attention modules in orientation‐magnitude‐response guided multi‐stream CNNs for human action recognition;IET Image Processing;2024-04-22

3. Human action recognition using multi-stream attention-based deep networks with heterogeneous data from overlapping sub-actions;Neural Computing and Applications;2024-03-27

4. A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision;Technologies;2024-01-23

5. A Vision-Based Method for Human Activity Recognition Using Local Binary Pattern;2023 13th International Conference on Computer and Knowledge Engineering (ICCKE);2023-11-01

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