Abstract
Human motion analysis has proven to be a great source of information for a wide range of applications. Several approaches for a detailed and accurate motion analysis have been proposed in the literature, as well as an almost proportional number of dedicated datasets. The relatively recent arrival of depth sensors contributed to an increasing interest in this research area and also to the emergence of a new type of motion datasets. This work focuses on a systematic review of publicly available depth-based datasets, encompassing human gait data which is used for person recognition and/or classification purposes. We have conducted this systematic review using the Scopus database. The herein presented survey, which to the best of our knowledge is the first one dedicated to this type of datasets, is intended to inform and aid researchers on the selection of the most suitable datasets to develop, test and compare their algorithms.
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A Datasets’ URLs
A Datasets’ URLs
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BVH MoCap Databse https://bit.ly/2kqdqtx
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Depth-Based Gait Dataset http://www.facweb.iitkgp.ac.in/~shamik/Gait/Dataset1.html
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GRIDDS http://gridds.ipvc.pt
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Kinect Gait Biometry Dataset https://bit.ly/2QbDu6U
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RGB-D Person Re-identification Dataset https://bit.ly/2HLXZU7
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SAIVT-DGD https://research.qut.edu.au/saivt/databases/saivt-dgd-database
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SZU RGB-D Gait Dataset http://yushiqi.cn
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TUM-GAID Database https://www.mmk.ei.tum.de/en/misc/tum-gaid-database
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UPCV Gait dataset http://www.upcv.upatras.gr/personal/kastaniotis/datasets.html
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Nunes, J.F., Moreira, P.M., Tavares, J.M.R.S. (2019). Benchmark RGB-D Gait Datasets: A Systematic Review. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2019. VipIMAGE 2019. Lecture Notes in Computational Vision and Biomechanics, vol 34. Springer, Cham. https://doi.org/10.1007/978-3-030-32040-9_38
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DOI: https://doi.org/10.1007/978-3-030-32040-9_38
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