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Human activity recognition with deep metric learners.

Martin, Kyle; Wijekoon, Anjana; Wiratunga, Nirmalie

Authors



Contributors

Stelios Kapetanakis
Editor

Hayley Borck
Editor

Abstract

Establishing a strong foundation for similarity-based return is a top priority in Case-Based Reasoning (CBR) systems. Deep Metric Learners (DMLs) are a group of neural network architectures which learn to optimise case representations for similarity-based return by training upon multiple cases simultaneously to incorporate relationship knowledge. This is particularly important in the Human Activity Recognition (HAR) domain, where understanding similarity between cases supports aspects such as personalisation and open-ended HAR. In this paper, we perform a short review of three DMLs and compare their performance across three HAR datasets. Our findings support research which indicates DMLs are valuable to improve similarity-based return and indicate that considering more cases simultaneously offers better performance.

Citation

MARTIN, K., WIJEKOON, A. and WIRATUNGA, N. 2019. Human activity recognition with deep metric learners. In Kapetanakis, S. and Borck, H. (eds.) Proceedings of the 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19), 8-12 September 2019, Otzenhausen, Germany. CEUR workshop proceedings, 2567. Aachen: CEUR-WS [online], pages 8-17. Available from: http://ceur-ws.org/Vol-2567/paper1.pdf

Conference Name 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19)
Conference Location Otzenhausen, Germany
Start Date Sep 8, 2019
End Date Sep 12, 2019
Acceptance Date Aug 9, 2019
Online Publication Date Jan 20, 2020
Publication Date Mar 4, 2020
Deposit Date Aug 15, 2019
Publicly Available Date Mar 28, 2024
Publisher CEUR Workshop Proceedings
Pages 8-17
Series Title CEUR workshop proceedings
Series Number 2567
Series ISSN 1613-0073
Keywords Human activity recognition; Deep metric learning; Deep learning; Metric learning; Matching networks
Public URL https://rgu-repository.worktribe.com/output/350316
Publisher URL http://ceur-ws.org/Vol-2567/paper1.pdf

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