3D Human Pose Analysis from Monocular Video by Simulated Annealed Particle Swarm Optimization
基于退火粒子群优化的单目视频人体姿态分析方法

In this paper we proposed a simulated annealing particle swarm optimism (SAPSO) based method for human pose estimation form monocular image sequences. First, we use principle component analysis (PCA) to learn the low-dimensional compact space of human pose, by which the aim of both reducing dimensionality and extracting the prior knowledge of human motion are achieved simultaneously. Pose is estimated on the compact subspace. In the optimizing step, we introduce particle swarm optimism to human pose estimation, and further, a SAPSO pose estimation method is proposed. And last we use SAPSO to estimate and track human pose in monocular videos separately. Experimental results demonstrate that the proposed method is more convergent and globally optimum, which can estimate and track human pose in monocular images effectively.