A Fast Teacher Pose Estimation Framework Base on Kernelized Correlation Filter and Spatial Transformed High-Resolution Network

摘要

Smart Education gradually becomes more of a concern for people, it providing sharable education resources, diversified teaching methods and data-based teaching evaluation methods. Virtual teacher is a part of smart education and it helps on improving learners’ cognitive level. The difficulty of virtual teacher technology lies in accurately recording and reproducing the actions and postures of teachers. It requires an accurate and fast teacher pose estimation algorithm as a support. This paper proposes a teacher pose estimation framework based on KCF and ST-HRN. Firstly, the target detection network YOLOv3 and the similarity algorithm based on SIFT feature are used to predict the position of the teacher in the video, and the KCF target tracking algorithm is used to accelerate the calculation. Secondly, based on the high-resolution network, the ST-HRN single-person pose estimation model is proposed and trained, and the average PCKH@0.5=90.2% accuracy is obtained on the MPII dataset. Finally, the framework is tested on class teaching videos and have 30.8% estimate speed promotion by implementing tracking algorithm.