Deep Residual Temporal Convolutional Networks for Skeleton-Based Human Action Recognition

Razieh Khamsehashari; Konrad Gadzicki; Christoph Zetzsche

doi:10.1007/978-3-030-34995-0_34

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Deep Residual Temporal Convolutional Networks for Skeleton-Based Human Action Recognition

Razieh Khamsehashari Konrad Gadzicki Christoph Zetzsche

Year: 2019 Lecture notes in computer science Pages: 376-385 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-34995-0_34

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Keywords:

Computer science Residual Action recognition Skeleton (computer programming) Artificial intelligence Convolutional neural network Human skeleton Action (physics) Pattern recognition (psychology) Speech recognition Computer vision Algorithm

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Human Pose and Action Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Gait Recognition and Analysis

Physical Sciences → Engineering → Biomedical Engineering

Deep Residual Temporal Convolutional Networks for Skeleton-Based Human Action Recognition

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