Citation
Tay, Nian Chi and Tee, Connie and Pang, Ying Han (2022) Few-shot Learning for Human Activity Recognition and Anomaly Detection. In: Postgraduate Colloquium December 2022, 1-15 December 2022, Multimedia University, Malaysia. (Unpublished)
Text
Poster_Tay-fist.pdf - Submitted Version Restricted to Repository staff only Download (437kB) |
Abstract
To formulate human activity recognition and anomaly detection methods with limited number of training data. •To propose a human activity reconstruction approach that can effectively locate primary human activity from a series of fine-grained secondary activities. •To evaluate the performance of the proposed human activity recognition and anomaly detection approaches.
Item Type: | Conference or Workshop Item (Poster) |
---|---|
Uncontrolled Keywords: | Human Activity Recognition |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics |
Divisions: | Faculty of Information Science and Technology (FIST) |
Depositing User: | Ms Rosnani Abd Wahab |
Date Deposited: | 27 Dec 2022 03:09 |
Last Modified: | 27 Dec 2022 03:09 |
URII: | http://shdl.mmu.edu.my/id/eprint/11014 |
Downloads
Downloads per month over past year
Edit (login required) |