MS-TCRNet: Multi-Stage Temporal Convolutional Recurrent Networks for action segmentation using sensor-augmented kinematics

Adam Goldbraikh; Omer Shubi; Or Rubin; Carla M. Pugh; Shlomi Laufer

doi:10.1016/j.patcog.2024.110778

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MS-TCRNet: Multi-Stage Temporal Convolutional Recurrent Networks for action segmentation using sensor-augmented kinematics

Adam Goldbraikh Omer Shubi Or Rubin Carla M. Pugh Shlomi Laufer

Year: 2024 Journal: Pattern Recognition Vol: 156 Pages: 110778-110778 Publisher: Elsevier BV

DOI: 10.1016/j.patcog.2024.110778

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Keywords:

Kinematics Artificial intelligence Stage (stratigraphy) Computer science Segmentation Action (physics) Computer vision Convolutional neural network Pattern recognition (psychology) Physics Geology

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Topics

Human Pose and Action Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Human Motion and Animation

Physical Sciences → Engineering → Control and Systems Engineering

Video Analysis and Summarization

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

MS-TCRNet: Multi-Stage Temporal Convolutional Recurrent Networks for action segmentation using sensor-augmented kinematics

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