Transfer learning with fine tuning for human action recognition from still images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-021-10753-y.pdf
Reference50 articles.
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2. Bhattacharya S, Shaw V, Singh PK, Sarkar DB (2020). SV-NET: a deep learning approach to video based human activity recognition. Proceedings of the eleventh international Conference on Soft Computing and Pattern Recognition, SoCPaR 2019, Hyderabad, India, December 13–15, 2019.
3. Chen C, Jafari R, Kehtarnavaz N (2015) UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. 2015 IEEE International Conference on Image Processing (ICIP), 168–172. IEEE
4. Chen C, Jafari R, Kehtarnavaz N (2017) A survey of depth and inertial sensor fusion for human action recognition. Multimed Tools Appl 76(3):4405–4425. https://doi.org/10.1007/s11042-015-3177-1
5. Clawson K, Jing M, Scotney B, Wang H, Liu J (2014) Human action recognition in video via fused optical flow and moment features – towards a hierarchical approach to complex scenario recognition BT - MultiMedia Modeling (C. Gurrin, F. Hopfgartner, W. Hurst, H. Johansen, H. Lee, & N. O'Connor, Eds.). Cham: Springer International Publishing.
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