A Human Body Part Semantic Segmentation Enabled Parsing for Human Pose Estimation

Human Body Part Semantic Segmentation and Human Pose estimation are considered to be essential for understanding human behaviours. Both of these tasks are correlated with each other. Employing them together in a unified framework to perform two distinct Human Centric Visual Analysis tasks simultaneously allows benefiting from each other. Taking advantage of the correlation between Human Body Part Semantic Segmentation and Human Pose Estimation, this paper proposes a unified framework that explores efficient context modelling. The framework simultaneously predicts the human body part semantic segmentation and pose estimation with high-quality results. The results extracted from the segmentation are used to predict the pose estimation task. An experimental analysis of the proposed framework is done on the benchmark LIP Dataset. The analysis of the results shows that the proposed framework outperforms the state-of-the-art by 7.3% when evaluated on mean IoU. Moreover, Mean Accuracy, Pixel Accuracy and PCKh are the other metrics used for the evaluation of the framework.