Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition

Article


Perera, Asanka G., Law, Yee Wei and Chahl, Javaan. 2019. "Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition." Drones. 3 (4). https://doi.org/10.3390/drones3040082
Article Title

Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition

ERA Journal ID212296
Article CategoryArticle
AuthorsPerera, Asanka G., Law, Yee Wei and Chahl, Javaan
Journal TitleDrones
Journal Citation3 (4)
Article Number82
Number of Pages16
Year2019
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2504-446X
Digital Object Identifier (DOI)https://doi.org/10.3390/drones3040082
Web Address (URL)https://www.mdpi.com/2504-446X/3/4/82
Abstract

Aerial human action recognition is an emerging topic in drone applications. Commercial drone platforms capable of detecting basic human actions such as hand gestures have been developed. However, a limited number of aerial video datasets are available to support increased research into aerial human action analysis. Most of the datasets are confined to indoor scenes or object tracking and many outdoor datasets do not have sufficient human body details to apply state-of-the-art machine learning techniques. To fill this gap and enable research in wider application areas, we present an action recognition dataset recorded in an outdoor setting. A free flying drone was used to record 13 dynamic human actions. The dataset contains 240 high-definition video clips consisting of 66,919 frames. All of the videos were recorded from low-altitude and at low speed to capture the maximum human pose details with relatively high resolution. This dataset should be useful to many research areas, including action recognition, surveillance, situational awareness, and gait analysis. To test the dataset, we evaluated the dataset with a pose-based convolutional neural network (P-CNN) and high-level pose feature (HLPF) descriptors. The overall baseline action recognition accuracy calculated using P-CNN was 75.92%.

Keywordsdrone; dataset; human action recognition; aerial video analysis; P-CNN
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204007. Control engineering, mechatronics and robotics
Byline AffiliationsUniversity of South Australia
Defence Science and Technology Group, Australia
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https://research.usq.edu.au/item/z77xz/drone-action-an-outdoor-recorded-drone-video-dataset-for-action-recognition

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