Al-Obaidi, S. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2019) Privacy protected recognition of activities of daily living in video. In: 3rd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2019). 3rd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2019), 25 Mar 2019, London, UK. Institution of Engineering and Technology ISBN 9781839530883
Abstract
This paper proposes a new method to protect the privacy while retaining the ability to accurately recognise the activities of daily living for video-based monitoring in ambient assisted living applications. The proposed method obfuscates the human appearance by modelling the temporal saliency in the monitoring video sequences. It mimics the functionality of neuromorphic cameras and explores the temporal saliency to generate a mask to anonymise the human appearance. Since the anonymising masks encapsulate the temporal saliency with respect to motion in the sequence, they provide a good basis for further utilisation in activity recognition, which is achieved by representing the HOG features on privacy masks. The proposed method has resulted in excellent anonymising performances compared using the cross correlation measures. In terms of activity recognition, the proposed method has resulted in 5.6% and 5.4% improvements of accuracies over other anonymisation methods for Weizmann and DHA datasets, respectively.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. This is an author-produced version of a paper subsequently published in Proceedings of TechAAL 2019. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | object detection; image representation; image colour analysis; data privacy; image motion analysis; image sequences; feature extraction; image recognition; video signal processing; security of data |
Dates: |
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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: | 17 Oct 2019 14:42 |
Last Modified: | 18 Oct 2019 01:56 |
Published Version: | https://digital-library.theiet.org/content/confere... |
Status: | Published |
Publisher: | Institution of Engineering and Technology |
Refereed: | Yes |
Identification Number: | 10.1049/cp.2019.0101 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150490 |