Stacked hourglass deep learning networks based on attention mechanism in multi-person pose estimation

Jiazhi Di; Ben Wang; Hua Hu

doi:10.1117/12.2668319

28 March 2023 Stacked hourglass deep learning networks based on attention mechanism in multi-person pose estimation

Jiazhi Di, Ben Wang, Hua Hu

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125660F (2023) https://doi.org/10.1117/12.2668319
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

In order to solve the problem of small-scale key point positioning accuracy in multiplayer pose estimation, using top-down method and advanced yolov4-tiny, this paper proposes an improved multi-person pose estimation method based on stacked hourglass deep learning network. The coordinate attention mechanism is introduced in the original hourglass network residual module to perform feature enhancement, which suppresses useless features and improves useful features, thus improving the recognition rate of small-scale joint points. From experiments, the index PCK@0.5 reaches 88.9% on MPII dataset, which verifies the effectiveness of our proposed method.

Citation Download Citation

Jiazhi Di, Ben Wang, and Hua Hu "Stacked hourglass deep learning networks based on attention mechanism in multi-person pose estimation", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125660F (28 March 2023); https://doi.org/10.1117/12.2668319

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