Deep Residual Temporal Convolutional Networks for Skeleton-Based Human Action Recognition
by , ,
Abstract:
Deep residual networks for action recognition based on skeleton data can avoid the degradation problem, and a 56-layer Res-Net has recently achieved good results. Since a much ``shallower'' 11-layer model (Res-TCN) with a temporal convolution network and a simplified residual unit achieved almost competitive performance, we investigate deep variants of Res-TCN and compare them to Res-Net architectures. Our results outperform the other approaches in this class of residual networks. Our investigation suggests that the resistance of deep residual networks to degradation is not only determined by the architecture but also by data and task properties.
Reference:
Deep Residual Temporal Convolutional Networks for Skeleton-Based Human Action Recognition (R. Khamsehashari, K. Gadzicki, C. Zetzsche), In Computer Vision Systems (Dimitrios Tzovaras, Dimitrios Giakoumis, Markus Vincze, Antonis Argyros, eds.), Springer International Publishing, 2019.
Bibtex Entry:
@InProceedings{Khamsehashari_IVCS_2019,
author="Khamsehashari, R. and Gadzicki, K. and Zetzsche, C.",
editor="Tzovaras, Dimitrios and Giakoumis, Dimitrios and Vincze, Markus and Argyros, Antonis",
title="Deep Residual Temporal Convolutional Networks for Skeleton-Based Human Action Recognition",
booktitle="Computer Vision Systems",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="376--385",
abstract="Deep residual networks for action recognition based on skeleton data can avoid the degradation problem, and a 56-layer Res-Net has recently achieved good results. Since a much ``shallower'' 11-layer model (Res-TCN) with a temporal convolution network and a simplified residual unit achieved almost competitive performance, we investigate deep variants of Res-TCN and compare them to Res-Net architectures. Our results outperform the other approaches in this class of residual networks. Our investigation suggests that the resistance of deep residual networks to degradation is not only determined by the architecture but also by data and task properties.",
isbn="978-3-030-34995-0",
urls="https://seafile.zfn.uni-bremen.de/f/d20b1db9e9e748b7b7a0/?dl=1",
}