A multiviewpoint outdoor dataset for human action recognition

Article

Perera, Asanka G., Law, Yee Wei, Ogunwa, Titilayo T and Chahl, Javaan. 2020. "A multiviewpoint outdoor dataset for human action recognition." IEEE Transactions on Human-Machine Systems. 50 (5), pp. 405-413. https://doi.org/10.1109/THMS.2020.2971958
Article Title

A multiviewpoint outdoor dataset for human action recognition

ERA Journal ID17886
Article CategoryArticle
AuthorsPerera, Asanka G., Law, Yee Wei, Ogunwa, Titilayo T and Chahl, Javaan
Journal TitleIEEE Transactions on Human-Machine Systems
Journal Citation50 (5), pp. 405-413
Number of Pages9
Year2020
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1094-6977
1558-2442
2168-2291
2168-2305
Digital Object Identifier (DOI)https://doi.org/10.1109/THMS.2020.2971958
Web Address (URL)https://ieeexplore.ieee.org/abstract/document/9005197/
Abstract

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, in this article we present a multiviewpoint outdoor action recognition dataset collected from YouTube and our own drone. The dataset consists of 20 dynamic human action classes, 2324 video clips, and 503 086 frames. All videos are cropped and resized to 720 × 720 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 evaluate the dataset with a two-stream convolutional neural network 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%.

KeywordsHuman action recognition; kernelized rank pooling; multiviewpoint; video dataset
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
Public Notes

The accessible file is the accepted version of the paper. Please refer to the URL for the published version.

Byline AffiliationsUniversity of South Australia
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