Human Action Recognition Based on Skeleton Information and Multi-Feature Fusion
Li Wang,
Bo Su,
Qunpo Liu
et al.
Abstract:Action assessment and feedback can effectively assist fitness practitioners in improving exercise benefits. In this paper, we address key challenges in human action recognition and assessment by proposing innovative methods that enhance performance while reducing computational complexity. Firstly, we present Oct-MobileNet, a lightweight backbone network, to overcome the limitations of the traditional OpenPose algorithm’s VGG19 network, which exhibits a large parameter size and high device requirements. Oct-Mob… Show more
“…Another limitation is that bottom-up methods cannot be trained from end-to-end, as the post-processing phase is non-differentiable and takes place outside the convolutional network's domain. Oct-MobileNet [14] employs octave convolution and attention mechanisms to improve the extraction of high-frequency features from the human body contour, resulting in enhanced accuracy with reduced model computational burden. MSTPose [15] learns texture features through CNN, captures spatial features of the images through the MST module, and employs one-dimensional vector regression to preserve the position-sensitive spatial sequential mapping structure of the transformer output.…”
Detecting posture changes of athletes in sports is an important task in teaching and training competitions, but its detection remains challenging due to the diversity and complexity of sports postures. This paper introduces a single-stage pose estimation algorithm named yolov8-sp. This algorithm enhances the original yolov8 architecture by incorporating the concept of multi-dimensional feature fusion and the attention mechanism for automatically capturing feature importance. Furthermore, in this paper, angle extraction is conducted for three crucial motion joints in the motion scene, with polynomial corrections applied across successive frames. In comparison with the baseline yolov8, the improved model significantly outperforms it in AP50 (average precision) aspects. Specifically, the model’s performance improves from 84.5 AP to 87.1 AP, and the performance of AP50–95, APM, and APL aspects also shows varying degrees of improvement; the joint angle detection accuracy under different sports scenarios is tested, and the overall accuracy is improved from 73.2% to 89.0%, which proves the feasibility of the method for posture estimation of the human body in sports and provides a reliable tool for the analysis of athletes’ joint angles.
“…Another limitation is that bottom-up methods cannot be trained from end-to-end, as the post-processing phase is non-differentiable and takes place outside the convolutional network's domain. Oct-MobileNet [14] employs octave convolution and attention mechanisms to improve the extraction of high-frequency features from the human body contour, resulting in enhanced accuracy with reduced model computational burden. MSTPose [15] learns texture features through CNN, captures spatial features of the images through the MST module, and employs one-dimensional vector regression to preserve the position-sensitive spatial sequential mapping structure of the transformer output.…”
Detecting posture changes of athletes in sports is an important task in teaching and training competitions, but its detection remains challenging due to the diversity and complexity of sports postures. This paper introduces a single-stage pose estimation algorithm named yolov8-sp. This algorithm enhances the original yolov8 architecture by incorporating the concept of multi-dimensional feature fusion and the attention mechanism for automatically capturing feature importance. Furthermore, in this paper, angle extraction is conducted for three crucial motion joints in the motion scene, with polynomial corrections applied across successive frames. In comparison with the baseline yolov8, the improved model significantly outperforms it in AP50 (average precision) aspects. Specifically, the model’s performance improves from 84.5 AP to 87.1 AP, and the performance of AP50–95, APM, and APL aspects also shows varying degrees of improvement; the joint angle detection accuracy under different sports scenarios is tested, and the overall accuracy is improved from 73.2% to 89.0%, which proves the feasibility of the method for posture estimation of the human body in sports and provides a reliable tool for the analysis of athletes’ joint angles.
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