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https://hdl.handle.net/2440/137725
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Type: | Conference paper |
Title: | The IKEA ASM Dataset: Understanding people assembling furniture through actions, objects and pose |
Author: | Ben-Shabat, Y. Yu, X. Saleh, F. Campbell, D. Rodriguez Opazo, C. Li, H. Gould, S. |
Citation: | Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2021), 2021, pp.846-858 |
Publisher: | IEEE |
Issue Date: | 2021 |
Series/Report no.: | IEEE Winter Conference on Applications of Computer Vision |
ISBN: | 9780738142661 |
ISSN: | 2472-6737 |
Conference Name: | IEEE Winter Conference on Applications of Computer Vision (WACV) (3 Jan 2021 - 9 Jan 2021 : virtual online) |
Statement of Responsibility: | Yizhak Ben-Shabat, Xin Yu, Fatemeh Saleh, Dylan Campbell, Cristian Rodriguez-Opazo, Hongdong Li, Stephen Gould |
Abstract: | The availability of a large labeled dataset is a key requirement for applying deep learning methods to solve various computer vision tasks. In the context of understanding human activities, existing public datasets, while large in size, are often limited to a single RGB camera and provide only per-frame or per-clip action annotations. To enable richer analysis and understanding of human activities, we introduce IKEA ASM—a three million frame, multi-view, furniture assembly video dataset that includes depth, atomic actions, object segmentation, and human poses. Additionally, we benchmark prominent methods for video action recognition, object segmentation and human pose estimation tasks on this challenging dataset. The dataset enables the development of holistic methods, which integrate multimodal and multi-view data to better perform on these tasks. |
Rights: | ©2021 IEEE |
DOI: | 10.1109/WACV48630.2021.00089 |
Grant ID: | http://purl.org/au-research/grants/arc/CE140100016 |
Published version: | https://ieeexplore.ieee.org/xpl/conhome/9423008/proceeding |
Appears in Collections: | Australian Institute for Machine Learning publications |
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