Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?

Conference paper


Nazmus Sakib, A. H. M., Basak, P., Doha Uddin, S., Mustavi Tasin, S. and Ahad, M. 2022. Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity? 3rd International Conference on Activity and Behavior Computing (ABC 2021). Online 22 - 23 Oct 2021 Springer Singapore. https://doi.org/10.1007/978-981-19-0361-8_10
AuthorsNazmus Sakib, A. H. M., Basak, P., Doha Uddin, S., Mustavi Tasin, S. and Ahad, M.
TypeConference paper
Abstract

Skeleton-based motion capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further adds to the difficulties. In this work, we have proposed an ensemble-based machine learning methodology that is targeted to work better on MoCap datasets. The experiments have been performed on the MoCap data given in the Bento Packaging Activity Recognition Challenge 2021. Bento is a Japanese word that resembles lunch-box. Upon processing the raw MoCap data at first, we have achieved an astonishing accuracy of 98% on tenfold cross-Validation and 82% on leave-one-out cross-validation by using the proposed ensemble model.

Year2022
Conference3rd International Conference on Activity and Behavior Computing (ABC 2021)
PublisherSpringer Singapore
Publication dates
Online04 May 2022
Publication process dates
Deposited26 Jul 2023
Journal citationpp. 167-180
ISSN2190-3018
Book titleSensor- and Video-Based Activity and Behavior Computing: Proceedings of 3rd International Conference on Activity and Behavior Computing (ABC 2021)
Book editorAhad, M.
Inoue, S.
Roggen, D.
Fujinami, K.
ISBN9789811903601
9789811903618
Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-19-0361-8_10
Web address (URL) of conference proceedingshttps://link.springer.com/book/10.1007/978-981-19-0361-8
Copyright holder© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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