A Monadic and Effective Frame Work for Single Human Pose Estimation of 2D Images and Videos

Abstract

Human pose estimation (HPE) is most dominant research areas in Computer Vision (CV) field. This technology will have huge implications. The significant applications include human computer interaction, activity recognition, video surveillance, motion recognition, etc. Different types of pose estimation are used for estimating the number of persons who are tracked. In this paper we are focusing on single 2D pose estimation. Present trends of pose estimation uses CNN based architectures for HPE and post statistical methods. In this work we propose a monadic frame work for both HPE and post processing into a single stage. It produces a human pose skeleton for a single two-dimensional image and videos. Here, take different modalities such as static image, static video and live video to generate different skeletons for SHPE. In this work we use pre-trained tensor flow based CNN model for pose estimation and also it produce better results compared to state of art in terms of performance metrics as PCP@.5, PCK@.5, and PCKh@.2.