A Multi-viewpoint Outdoor Dataset for Human Action Recognition

Advancements in deep neural networks have contributed to near perfect results for many computer vision problems such as object recognition, face recognition and pose estimation. However, human action recognition is still far from human-level performance. Owing to the articulated nature of the human...

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Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Perera, Asanka G, Yee Wei Law, Ogunwa, Titilayo T, Chahl, Javaan
Format: Article
Language:English
Subjects:
Artificial neural networks
Aspect ratio
Computer vision
Datasets
Face recognition
Human activity recognition
Human motion
Human performance
Object recognition
Situational awareness
Video
Online Access:Get full text
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Summary:Advancements in deep neural networks have contributed to near perfect results for many computer vision problems such as object recognition, face recognition and pose estimation. However, human action recognition is still far from human-level performance. Owing to the articulated nature of the human body, it is challenging to detect an action from multiple viewpoints, particularly from an aerial viewpoint. This is further compounded by a scarcity of datasets that cover multiple viewpoints of actions. To fill this gap and enable research in wider application areas, we present a multi-viewpoint outdoor action recognition dataset collected from YouTube and our own drone. The dataset consists of 20 dynamic human action classes, 2324 video clips and 503086 frames. All videos are cropped and resized to 720x720 without distorting the original aspect ratio of the human subjects in videos. This dataset should be useful to many research areas including action recognition, surveillance and situational awareness. We evaluated the dataset with a two-stream CNN architecture coupled with a recently proposed temporal pooling scheme called kernelized rank pooling that produces nonlinear feature subspace representations. The overall baseline action recognition accuracy is 74.0%.
ISSN:2331-8422
DOI:10.48550/arxiv.2110.04119