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Towards a Skeleton-Based Action Recognition For Realistic Scenarios

2019-05-14
Cagatay Odabasi, Jewel Jose

Abstract

Understanding human actions is a crucial problem for service robots. However, the general trend in Action Recognition is developing and testing these systems on structured datasets. That’s why this work presents a practical Skeleton-based Action Recognition framework which can be used in realistic scenarios. Our results show that although non-augmented and non-normalized data may yield comparable results on the test split of the dataset, it is far from being useful on another dataset which is a manually collected data.

Abstract (translated by Google)
URL

https://arxiv.org/abs/1905.05420

PDF

https://arxiv.org/pdf/1905.05420


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