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RHM: Robot House Multi-view Human Activity Recognition Dataset

Authors:
Mohammad Hossein Bamorovat Abadi
Mohamad Reza Shahabian Alashti
Patrick Holthaus
Catherine Menon
Farshid Amirabdollahian

Keywords: Human Action Recognition; Human-Robot Interaction.

Abstract:
With the recent increased development of deep neural networks and dataset capabilities, the Human Action Recognition (HAR) domain is growing rapidly in terms of both the available datasets and deep models. Despite this, there are some lacks of datasets specifically covering the Robotics field and Human-Robot interaction. We prepare and introduce a new multi-view dataset to address this. The Robot House Multi-View (RHM) dataset contains four views: Front, Back, Ceiling (Omni), and robot-views. There are 14 classes with 6701 video clips for each view, making a total of 26804 video clips for the four views. The lengths of the video clips are between 1 to 5 seconds. The videos with the same number and the same classes are synchronized in different views. In the second part of this paper, we consider how single streams afford activity recognition using established state-of-the-art models. We then assess the affordance for each view based on information theoretic modelling and mutual information concept. Furthermore, we benchmark the performance of different views, thus establishing the strengths and weaknesses of each view relevant to their information content and performance of the benchmark. Our results lead us to conclude that multi-view and multi-stream activity recognition has the added potential to improve activity recognition results. The RHM dataset is available at {href{https://robothouse-dev.herts.ac.uk/datasets/RHM/HAR-1/}{Robot House}}.

Pages: 159 to 166

Copyright: Copyright (c) IARIA, 2023

Publication date: April 24, 2023

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-68558-078-0

Location: Venice, Italy

Dates: from April 24, 2023 to April 28, 2023