Annotated skeletal datasets [initial release]

As SPOTER works on top of sequences of signers' skeletal data extracted from videos, we wanted to eliminate the computational demands of such annotation for each training run by pre-collecting this. For this reason and reproducibility, we are open-sourcing this data along with the code as well.

This data is shared under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license allowing only for non-commercial usage only.

We employed the WLASL100 and LSA64 datasets for our experiments. Their corresponding citations can be found below:

@inproceedings{li2020word,
    title={Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison},
    author={Li, Dongxu and Rodriguez, Cristian and Yu, Xin and Li, Hongdong},
    booktitle={The IEEE Winter Conference on Applications of Computer Vision},
    pages={1459--1469},
    year={2020}
}

@inproceedings{ronchetti2016lsa64,
    title={LSA64: an Argentinian sign language dataset},
    author={Ronchetti, Franco and Quiroga, Facundo and Estrebou, C{\'e}sar Armando and Lanzarini, Laura Cristina and Rosete, Alejandro},
    booktitle={XXII Congreso Argentino de Ciencias de la Computaci{\'o}n (CACIC 2016).},
    year={2016}
}