NDLI: Collecting public RGB-D datasets for human daily activity recognition

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Collecting public RGB-D datasets for human daily activity recognition

Content Provider	SAGE Publishing
Author	Wu, Hanbo Ma, Xin Zhang, Zhimeng Wang, Haibo Li, Yibin
Copyright Year	2017
Abstract	Human daily activity recognition has been a hot spot in the field of computer vision for many decades. Despite best efforts, activity recognition in naturally uncontrolled settings remains a challenging problem. Recently, by being able to perceive depth and visual cues simultaneously, RGB-D cameras greatly boost the performance of activity recognition. However, due to some practical difficulties, the publicly available RGB-D data sets are not sufficiently large for benchmarking when considering the diversity of their activities, subjects, and background. This severely affects the applicability of complicated learning-based recognition approaches. To address the issue, this article provides a large-scale RGB-D activity data set by merging five public RGB-D data sets that differ from each other on many aspects such as length of actions, nationality of subjects, or camera angles. This data set comprises 4528 samples depicting 7 action categories (up to 46 subcategories) performed by 74 subjects. To verify the challengeness of the data set, three feature representation methods are evaluated, which are depth motion maps, spatiotemporal depth cuboid similarity feature, and curvature space scale. Results show that the merged large-scale data set is more realistic and challenging and therefore more suitable for benchmarking.
Related Links	https://journals.sagepub.com/doi/pdf/10.1177/1729881417709079?download=true
ISSN	17298806
Issue Number	4
Volume Number	14
Journal	International Journal of Advanced Robotic Systems (ARX)
e-ISSN	17298814
DOI	10.1177/1729881417709079
Language	English
Publisher	Sage Publications UK
Publisher Date	2017-07-04
Publisher Place	London
Access Restriction	Open
Rights Holder	© The Author(s) 2017
Subject Keyword	depth motion maps Human daily activity recognition curvature space scale public RGB-D data sets merging large-scale RGB-D activity data set depth cuboid similarity feature
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Science Applications Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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