Human shooting pose accuracy recognition algorithm based on optimized YOLOv5
Zhanjun Chang, Deguo Yang · Third International Conference on Computer Vision and Data Mining (ICCVDM 2022) · 2023
Aiming at the practical problem that it is difficult to accurately and accurately recognize the shooting pose, this paper proposes a human shooting pose recognition algorithm based on optimized YOLOv5. Firstly, YOLOv5 detection algorithm is adopted in the target detection module of Alphapose. Based on YOLOv5, a four-scale feature fusion structure is proposed to enhance the detection ability of smaller scale objects by adding a small target detection layer. Secondly, the C3 module in the original small-scale detection layer is improved, and the original bottleneck module is stacked by three Transformer encoders, so as to improve the ability of YOLOv5 to learn the feature information of small targets. In addition, this paper proposes a new algorithm based on alignment and matching of key points to evaluate the accuracy of shot pose. The analysis of test results shows that the improved YOLOv5 model can achieve 90.2% recognition rate and 89.5% average accuracy on the human detection algorithm, and the recognition efficiency of key points can be improved from the original 28 frames per second to 105 frames per second. Combined with the human key point shooting evaluation algorithm, The scheme proposed in this paper can meet the requirement of accuracy and real-time of recognizing human shooting posture.