强化先验骨架结构的轻量型高效人体姿态估计

Lightweight and efficient human pose estimation with enhanced priori skeleton structure

为了更好地利用人体姿态关键点特有的分布属性,提出强化先验骨架结构的轻量型高效人体姿态估计方法.利用高分辨率网络较好地保留空间位置信息,为了进一步降低模型参数量,提出轻量倒残差模块.设计体位强化模块,利用全局空间特征和上下文信息强化躯干位置的先验信息及关键点之间的联系.针对多分辨率特征图像融合时,像素位置模糊、卷积核优化方向偏移导致关键点空间特征信息遗失的问题,提出方向强化卷积模块,利用躯干上关键点分布的水平和垂直方向特性,高效融合关键点先验分布.实验结果表明,利用该网络,可以高效地估计人体姿态.与基准网络相比,该模型在COCO测试集上的平均精度达到78.4,参数量减少了17.4×10^(6),兼顾精度与效率.

A lightweight and efficient human pose estimation method with an enhanced priori skeleton structure was proposed to better utilize the unique distribution properties of human pose keypoints.The high-resolution network was used to preserve spatial location information better.The lightweight inverse residual module was employed to reduce the number of model parameters.The postural enhancement module was designed to strengthen the priori information of human pose and the connection between human pose keypoints using global spatial feature information and context information.The direction-enhanced convolution module was proposed to address the problem of missing spatial feature information of keypoints caused by blurred pixel positions and directional shifts of convolution kernel optimization when fusing multi-resolution feature images.The prior distribution of keypoints was combined by utilizing the properties of the horizontal and vertical directions of the keypoints on the torso.The experimental results demonstrate that the network can efficiently estimate human pose.The model achieves an average precision score of 78.4 on the COCO test-dev set and reduces the number of parameters by 17.4×10^(6) compared with the benchmark network,balancing accuracy and efficiency.