Human pose estimation based on region refined network Online publication date: Mon, 12-Oct-2020
by Minghui Wu; Pintong Zhao; Guangjie Zhang; Huifeng Wu
International Journal of Intelligent Internet of Things Computing (IJIITC), Vol. 1, No. 2, 2020
Abstract: With the development of detection technology based on deep learning, the keypoint detection of the human body has gradually formed a theoretical system based on convolutional neural networks. In this context, the keypoints of the predicted and real values in the model follow the statistical deviation law of neighbourhood, and an improved model for refined prediction of keypoints is proposed. The original one-stage detection network is transformed into a two-stage end-to-end detection network. The detection error of the typical model in the keypoint neighbourhood is reduced, and the AP of the model on the COCO2017 dataset has an increase of 1 percentage point. This paper will detail in the improvement work the structure and parameters of the network, the training and prediction process of the network, the network supervision and loss function, and finally the experimental results on the COCO2017 dataset.
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