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Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark Read chapter Read first chapter

2021 | OriginalPaper | Chapter

ARID: A New Dataset for Recognizing Action in the Dark

Authors : Yuecong Xu, Jianfei Yang, Haozhi Cao, Kezhi Mao, Jianxiong Yin, Simon See

Published in: Deep Learning for Human Activity Recognition

Publisher: Springer Singapore

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Abstract

The task of action recognition in dark videos is useful in various scenarios, e.g., night surveillance and self-driving at night. Though progress has been made in action recognition task for videos in normal illumination, few have studied action recognition in the dark, partly due to the lack of sufficient datasets for such a task. In this paper, we explored the task of action recognition in dark videos. We bridge the gap of the lack of data by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset. It consists of 3,784 video clips with 11 action categories. To the best of our knowledge, it is the first dataset focused on human actions in dark videos. To gain further understanding of our ARID dataset, we analyze our dataset in detail and showed its necessity over synthetic dark videos. Additionally, we benchmark the performance of current action recognition models on our dataset and explored potential methods for increasing their performances. We show that current action recognition models and frame enhancement methods may not be effective solutions for the task of action recognition in dark videos (data available at https://​xuyu0010.​github.​io/​arid).

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previous chapter Resource-Constrained Federated Learning with Heterogeneous Labels and Models for Human Activity Recognition
next chapter Single Run Action Detector over Video Stream - A Privacy Preserving Approach
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Metadata
Title
ARID: A New Dataset for Recognizing Action in the Dark
Authors
Yuecong Xu
Jianfei Yang
Haozhi Cao
Kezhi Mao
Jianxiong Yin
Simon See
Copyright Year
2021
Publisher
Springer Singapore
Book
Deep Learning for Human Activity Recognition

Print ISBN: 978-981-16-0574-1

Electronic ISBN: 978-981-16-0575-8

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-16-0575-8_6

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