结合形变模型的人体姿态估计优化算法

Optimization algorithm for estimating the human pose by using the morphable model

为了解决现有的基于人体形变模型的姿态估计算法容易出现误差、组成的运动序列不连贯等问题,提出利用深度相机获取的视频数据、点云数据进行优化的方法。对于视频数据:首先使用神经网络从视频每一帧彩色图像中提取模型参数,再利用人体关键点和轮廓的约束对参数进行优化求解,最后结合视频序列的帧间连贯性对视频全部帧的姿态估计结果进行误差纠正,使所得的运动序列更加流畅平滑。此外,为了进一步提升算法的精度,利用深度图所得点云与对应彩色图所得模型作为联合输入,然后利用点云与模型对应点的距离约束进行优化求解,最终得到一个与人体真实姿态相似的结果。将该算法与同类算法分别在公开数据集和真实数据上进行定性及定量的比较,实验结果表明,该算法能有效地纠正单帧姿态估计结果中出现的误差及运动不连续等问题,且在利用点云数据优化后,大幅提高了算法的精确度。

An optimization algorithm is proposed utilizing the video data and point cloud data captured by the depth camera to solve the problems such as error-proneness and incoherence of motion sequence caused by the existing human pose estimation algorithms based on the morphable model.For video data,the neural network is first used in extracting the model parameters from each color image frame.Next,the human key-points and contour constraint are considered to optimize the above parameters.Then the coherence between every two consecutive frames is utilized to correct the error of pose estimation,thus making the resulting motion sequence smoother.In addition,the point cloud and the model obtained from the corresponding color image frame are used as the joint input to further improve the estimation accuracy.Finally,the distance between the point cloud and the corresponding point of the model is constrained to be as small as possible to obtain a more reasonable solution.The proposed algorithm and the state-of-the-art algorithms are compared qualitatively and quantitatively on the data set and real video set.Experimental results show that the algorithm can effectively correct the error and incoherence in the single-frame pose estimation results and greatly improve the accuracy when using point cloud data optimization.