关键词: 人体姿态估计, 深度学习, 卷积神经网络, 加权支持向量数据描述, 线性组合

Abstract: The Convolutional Neural Network（CNN） is the most successful deep learning model for human pose estimation, but there are still have some deficiencies such as different image regions when extracting image features and different abstract features extracted by CNN are treated equally. For overcoming these two deficiencies, a joint appearance model based on improved CNN and weighted Support Vector Data Description（SVDD） algorithm is proposed, and a human pose estimation algorithm is designed. The convolutional operations for different image regions are assigned to different weight coefficients, which reflects their different effects. The weighted SVDD algorithm is used to build joint appearance sub-model for each abstract feature, and then a new joint appearance model is built by linear combination of all sub-models with different weights, which reflects different characteristics of different abstract features. The simulation results show that the proposed algorithm has higher estimation accuracy than the human pose estimation algorithms based on the traditional CNN.

Key words: human pose estimation, deep learning, Convolutional Neural Network（CNN）, weighted support vector data description, linear combination