Al-Obaidi, S. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2019) Temporal salience based human action recognition. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12-17 May 2019, Brighton, United Kingdom. IEEE , pp. 2017-2021. ISBN 978-1-4799-8131-1
Abstract
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. the salience region, and obtains the action representation using solely the saliency information. Outperforming results on Weizmann, DHA and KTH datasets confirm the efficiency of the proposed approach as compared to the state-of-the-art methods, in terms of accuracy and robustness to the variations inside the action and similarities among actions. The proposed method outperforms by 2.7% with DHA, 1% with KTH and it is comparable in the case of Weizmann.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Human Action Recognition (HAR); Temporal Salience; Salience-based HAR; Histogram of Oriented Gradients of Salience (HOG-S) |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Jun 2019 12:08 |
Last Modified: | 17 Apr 2020 00:39 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/icassp.2019.8682569 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145958 |