Ivekovic Spela, Trucco Emanuele, Petillot Yvan R
School of Computing, University of Dundee, Dundee DD1 4HN, UK.
Evol Comput. 2008 Winter;16(4):509-28. doi: 10.1162/evco.2008.16.4.509.
In this paper we address the problem of human body pose estimation from still images. A multi-view set of images of a person sitting at a table is acquired and the pose estimated. Reliable and efficient pose estimation from still images represents an important part of more complex algorithms, such as tracking human body pose in a video sequence, where it can be used to automatically initialise the tracker on the first frame. The quality of the initialisation influences the performance of the tracker in the subsequent frames. We formulate the body pose estimation as an analysis-by-synthesis optimisation algorithm, where a generic 3D human body model is used to illustrate the pose and the silhouettes extracted from the images are used as constraints. A simple test with gradient descent optimisation run from randomly selected initial positions in the search space shows that a more powerful optimisation method is required. We investigate the suitability of the Particle Swarm Optimisation (PSO) for solving this problem and compare its performance with an equivalent algorithm using Simulated Annealing (SA). Our tests show that the PSO outperforms the SA in terms of accuracy and consistency of the results, as well as speed of convergence.
在本文中，我们探讨了从静态图像估计人体姿态的问题。采集了一个人坐在桌前的多视角图像集，并对姿态进行了估计。从静态图像中进行可靠且高效的姿态估计是更复杂算法的重要组成部分，比如在视频序列中跟踪人体姿态，在这种情况下，它可用于在第一帧自动初始化跟踪器。初始化的质量会影响后续帧中跟踪器的性能。我们将人体姿态估计表述为一种通过合成进行分析的优化算法，其中使用通用的三维人体模型来描述姿态，并将从图像中提取的轮廓用作约束条件。从搜索空间中随机选择的初始位置运行梯度下降优化进行的一个简单测试表明，需要一种更强大的优化方法。我们研究了粒子群优化算法（PSO）解决此问题的适用性，并将其性能与使用模拟退火算法（SA）的等效算法进行比较。我们的测试表明，在结果的准确性、一致性以及收敛速度方面，PSO优于SA。
