基于退火粒子群优化的单目视频人体姿态分析方法

提出一种基于退火粒子群优化(Simulated annealing particle swarm optimism, SAPSO)的单目视频人体姿态分析方法. 该方法具有以下特点: 首先, 利用运动捕获数据采用主成分分析方法(Principle component analysis, PCA)得到更能反映人体运动本质的姿态紧致空间, 并在此低维空间中进行姿态分析, 提高了姿态分析的准确性和效率; 其次, 将粒子群优化应用到姿态分析中, 并提出退火粒子群优化姿态分析方法, 该方法具有良好的收敛性和全局最优能力; 再次, 基于退火粒子群优化姿态分析方法, 实现了基于单目视频的人体姿态估计和跟踪. 实验结果表明, 本文方法不仅具有良好的计算效率, 同时具有良好的收敛性和全局搜索能力, 能准确分析单目视频中的人体姿态.

3D Human Pose Analysis from Monocular Video by Simulated Annealed Particle Swarm Optimization

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.