An Accelerated Algorithm of Human Pose Estimation for Video
In order to solve the problem of slow inference speed caused by the large number of parameters and com-putation of high performance human pose estimation algorithm for video,an improved algorithm of human pose estimation based on High Resolution Network(HRNet)is proposed.The algorithm uses interframe detection and de-jittering optimiza-tion in the detection process to optimize the human detection process.For the pose estimation network,the S-HRNet is obtained by redesigning the HRNet using ShuffleUnitV2 component to improve the utilization of the network.The experimen-tal results show that the proposed accelerated algorithm for human pose estimation has a total inference time of 356ms in the public data set COCO training set,while the original algorithm has a total inference time of 992ms,which effectively im-proves the inference speed.The improved algorithm solves the problems of large parameters and slow inference speed of the original HRNet model,while maintaining a certain performance,providing a suitable algorithm for actual deployment.
human pose estimationhigh-resolution networksinference speednetwork structure
万方数据
维普
