Temporal multiscale feature network for skeleton-based action recognition

Xuzhao Liu

doi:10.1117/12.3039067

13 September 2024 Temporal multiscale feature network for skeleton-based action recognition

Xuzhao Liu

Proceedings Volume 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024); 132540X (2024) https://doi.org/10.1117/12.3039067
Event: Fourth International Conference on Optics and Image Processing (ICOIP 2024), 2024, Chongqing, China

Abstract

Skeleton-based action recognition utilizes human skeletal data to identify and analyze human behaviors , which is widely applied in various scenarios such as intelligent surveillance, virtual reality, and autonomous driving. Since human actions usually have different durations, recognizing skeleton-based actions accurately and efficiently remains a challenge. To address the issue of temporal scale, this paper introduces a temporal multi-scale feature network (TMSF-Net) designed to enhance the recognition of skeleton-based actions. Specifically, TMSF-Net introduces a multi-scale temporal convolution module (MSTCM) to flexibly adjust the temporal receptive field of the network, enabling it to focus more on action-related regions. Additionally, TMSF-Net incorporates a Global Filter Module (GFM) to enhance the interaction among joint points across spatial and temporal dimensions. and adapt to different action scenarios. The efficacy of the proposed approach is demonstrated through experimental validation on two public datasets dedicated to skeleton action recognition.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuzhao Liu "Temporal multiscale feature network for skeleton-based action recognition", Proc. SPIE 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024), 132540X (13 September 2024); https://doi.org/10.1117/12.3039067

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KEYWORDS

Action recognition

Convolution

Tunable filters

Feature extraction

Ablation

Calibration

Network architectures

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