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NTU60-X: Towards Skeleton-based Recognition of Subtle Human Actions

2021-01-27 16:33:51
Anirudh Thatipelli, Neel Trivedi, Ravi Kiran Sarvadevabhatla
arXiv_CV
       
Abstract

The lack of fine-grained joints such as hand fingers is a fundamental performance bottleneck for state of the art skeleton action recognition models trained on the largest action recognition dataset, NTU-RGBD. To address this bottleneck, we introduce a new skeleton based human action dataset - NTU60-X. In addition to the 25 body joints for each skeleton as in NTU-RGBD, NTU60-X dataset includes finger and facial joints, enabling a richer skeleton representation. We appropriately modify the state of the art approaches to enable training using the introduced dataset. Our results demonstrate the effectiveness of NTU60-X in overcoming the aforementioned bottleneck and improve state of the art performance, overall and on hitherto worst performing action categories.

Abstract (translated)

URL

https://arxiv.org/abs/2101.11529

PDF

https://arxiv.org/pdf/2101.11529.pdf

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