Abstract
Several approaches based on human gait have been proposed in the literature, either for medical research reasons, smart surveillance, human-machine interaction, or other purposes, whose validation highly depends on the access to common input data through available datasets, enabling a coherent performance comparison. The advent of depth sensors leveraged the emergence of novel approaches and, consequently, the usage of new datasets. In this work we present the GRIDDS - A Gait Recognition Image and Depth Dataset, a new and publicly available gait depth-based dataset that can be used mostly for person and gender recognition purposes.
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Notes
- 1.
Available at https://github.com/joaofnunes/gridds.
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Nunes, J.F., Moreira, P.M., Tavares, J.M.R.S. (2019). GRIDDS - A Gait Recognition Image and Depth Dataset. 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_36
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DOI: https://doi.org/10.1007/978-3-030-32040-9_36
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