基于光流的快速人体姿态估计

针对目前深度学习领域人体姿态估计算法计算复杂度高的问题，提出了一种基于光流的快速人体姿态估计算法.在原算法的基础上，首先利用视频帧之间的时间相关性，将原始视频序列分为关键帧和非关键帧分别处理（相邻两关键帧之间的图像和前向关键帧组成一个视频帧组，同一视频帧组内的视频帧相似），仅在关键帧上运用人体姿态估计算法，并通过轻量级光流场将关键帧识别结果传播到其他非关键帧.其次针对视频中运动场的动态特性，提出一种基于局部光流场的自适应关键帧检测算法，以根据视频的局部时域特性确定视频关键帧的位置.在OutdoorPose和HumanEvaI数据集上的实验结果表明，对于存在背景复杂、部件遮挡等问题的视频序列中，所提算法较原算法检测性能略有提升，检测速度平均可提升89.6%.

Fast Human Pose Estimation Based on Optical Flow

Aiming at the problem of high computational complexity of human pose estimation algorithm in deep learning field, a fast human pose estimation algorithm based on optical flow is proposed. Based on the original algorithm, using the time correlation between video frames, the original video sequence is divided into key frames and non-key frames, which are processed respectively (the images between two adjacent key frames and the forward key frame compose a video frame group, which is similar to the frames in the same video frame group), the human pose estimation algorithm is applied only to the key frames, and the key frame recognition result is propagated to other non-key frames through the lightweight optical flow field. Secondly, aiming at the dynamic characteristics of the video field, this study proposes an adaptive key frame detection algorithm based on local optical flow to determine the position of the key frame of video according to the local time-domain characteristics of the video. The experimental results in OutdoorPose and HumanEvaI data sets show that the detection performance of the proposed algorithm is slightly higher than the original algorithm in the video sequences with complex background and component occlusion. The detection speed is increased by 89.6% in average.