Summary of the Bento Packaging Activity Recognition Challenge

Adachi, Keiichiro; Alia, Sayeda Shamma; Nahid, Nazmun; Kaneko, Haru; Lago, Paula; Inoue, Shunsuke

doi:10.1007/978-981-19-0361-8_17

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…The size of the dataset to perform such experiments is another important factor because small datasets often limit the scope of work. In this paper, our team Nirban proposes an approach to solve these issues and detect such activities in the Bento Packaging Activity Recognition Challenge 2021 [3,1].…”

Section: Introductionmentioning

confidence: 99%

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

Sakib

Basak

Uddin

et al. 2022

Sensor- And Video-Based Activity and Behavior Computing

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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 10-fold Cross-Validation and 82% on Leave-One-Out-Cross-Validation by using the proposed ensemble model.

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Section: Introductionmentioning

confidence: 99%