【摘要】 在复杂场景下，针对传统人体姿态估计模型中关键点分配算法存在正确率低、资源分配不均等优化问题，在OpenPose模型的基础上提出了一种改进的匈牙利算法，该算法在传统数学模型的基础上采用亲和度向量场与邻接矩阵结合的方式，通过对矩阵内的数值处理，来获取关键点的最佳匹配。实验表明，改进算法的运行时间有一定的减短，同时保证在100×100以内的矩阵中运算的精确度误差率不高于0.014，且在使用本地图像测试的实验中证明了模型的可行性和性能的提升。更多还原

【Abstract】 In complex scenarios, there are optimization problems such as low accuracy and uneven resource allocation in the key point allocation algorithm in the traditional human pose estimation model. An improved Hungarian algorithm based on the OpenPose model is proposed. Based on the traditional mathematical model, the algorithm combines the affinity vec-tor field with the adjacency matrix, and obtains the optimal matching of key points through the numerical processing in the matrix. Experimental results show that such algorithm can reduce the running time to a certain extent, while ensuring that the accuracy error rate of the operation in a matrix within 100 × 100 is not higher than 0. 014. In experiments using local image testing, the feasibility and performance improvement of the model are verified.更多还原