Abstract
Benefiting from the extensive use of depth sensors, human action recognition plays an important role in a great number of works, including human-computer interaction, security monitoring, motion-sensing games, medical care. In most recent works, depth cameras have been used to capture required action data for action identification. Especially, feature extraction based on skeleton information has made satisfactory achievements in action recognition and has been gradually extended to various algorithms. However, the research of view invariance is not deep enough. To improve the performance of skeleton recognition, the paper proposes an improved asymmetric convolution adaptive network, achieving desirable results on the public benchmark dataset of NTU RGB + D 60. The model combines an advanced adaptive module and asymmetric convolution blocks, which can effectively extract features from the original skeleton. Ablation studies and comparative experiments indicate that the performance of the model is better than many of the most state-of-the-art algorithms.
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Ma, T., Yu, J., Gao, H., Ju, Z. (2022). Asymmetric Convolution View Adaptation Networks for Skeleton-Based Human Action Recognition. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_17
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