Peng, Gang ; Zheng, Yuezhi ; Li, Jianfeng ; Yang, Jin
Współtwórca:Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Tytuł:A single upper limb pose estimation method based on the improved stacked hourglass network
Tytuł publikacji grupowej: Temat i słowa kluczowe:convolutional neural networks ; stacked hourglass network ; skeleton key point ; single upper limb pose estimation ; human?machine coordination
Abstract:At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation. ; For use in human-machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model is designed. A deconvolution layer is employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps. ; Integral regression is used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments show that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance.
Wydawca:Zielona Góra: Uniwersytet Zielonogórski
Data wydania: Typ zasobu: DOI: Strony: Źródło:AMCS, volume 31, number 1 (2021) ; kliknij tutaj, żeby przejść
Jezyk: Licencja CC BY 4.0: Prawa do dysponowania publikacją: