关键词: 人体姿态估计, 部位外观模型, 梯度方向直方图, 颜色, 可能性C聚类算法

Abstract: The existing human pose estimation algorithm always get the lower accuracy for those images with very poor light conditions and low color contrast. For solving the problem, a more suitable part appearance model based on the Possibilistic C-Means（PCM） clustering algorithm is built by using Histogram of Oriented Gradient（HOG） and color features, and a new human pose estimation algorithm based on the fusion of HOG and color features is proposed. The part appearance model is selected automatically according to the image to be processed, the existing part appearance model based on the fusion of HOG and color features is selected if both the light conditions and color contrast are good, and otherwise the part appearance model based on PCM clustering algorithm is chosen. Simulation results show that the established part appearance model can represent the appearance of real human part, which is from images with very poor light conditions and low color contrast, more accurately, the proposed human posture estimation algorithm can get more accurate estimation results for various types of images to be processed.

Key words: human pose estimation, part appearance model, histogram of oriented gradient, color, possibilistic C-means clustering algorithm