基于Fast-OpenPose的仰卧起坐姿态估计研究

Research on sit-up posture estimation based on Fast-OpenPose

当前,许多学校体质测试项目中的仰卧起坐测试仍需通过手动计数,这不仅耗费人力,而且效率较低。为了促进体质健身的智能化发展,提出了一种基于人体姿态估计模型Fast-OpenPose和支持向量机(support vector machine,SVM)融合实现的仰卧起坐行为计数方法。通过OpenPose检测出仰卧起坐连续视频流中人体关键点的位置信息,再用SVM对获取到的每一帧人体关键点的坐标数据进行动作特征分类。鉴于原OpenPose网络复杂度高、模型参数量大、检测耗时长的缺陷,用FasterNet对其主干特征提取部分进行轻量化改进,并在预测分支中优化更为高效的单分支网络结构和卷积类型,最后引入空间注意力(spatial group-wise enhance,SGE)来弥补精度损失。在CoCo2017数据集的基础上,额外扩充1000张仰卧起坐场景的图片数据进行模型训练,实验结果表明,改进后的Fast-OpenPose在损失部分精度但不影响仰卧起坐姿态估计的情况下,模型参数量缩减近80%,关键点检测速度提升110%。与同系列其他改进模型相比,在保持相近平均精度均值(mAP)的同时,更具有轻量化与速度优势。

At present,the sit-up test in many school physical fitness test projects still needs to be manually counted,which not only consumes manpower,but also is inefficient.In order to promote the intelligent development of physical fitness,this paper proposes a method for counting sit-up behavior based on a fusion implementation of the Fast-OpenPose human posture estimation model and support vector machine(SVM).The position information of the key points of the human body in the continuous video stream of sit-ups is detected by OpenPose,and the SVM is used to classify the motion features of the coordinate data of each frame of the key points of the human body.In view of the shortcomings of the original OpenPose network,such as high complexity,large number of model parameters and long detection time,this paper uses FasterNet to lightweightly improve its main feature extraction part,and optimizes the more efficient single branch network structure and convolution type in the prediction branch.Finally,spatial group-wise enhance(SGE)is introduced to make up for the loss of accuracy.Based on the CoCo2017 dataset,an additional 1000 image data of sit-up scene are expanded for model training,and the experimental results show that the improved Fast-OpenPose reduces the number of model parameters by nearly 80%and improves the speed of keypoint detection by 110%,while losing part of the accuracy but not affecting the sit-up pose estimation.Compared with other improved models in the same series,it has more lightweight and speed advantages while maintaining similar mAP values.