Qiao Hong, Zhang Peng, Zhang Bo, Zheng Suiwu
Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
IEEE Trans Syst Man Cybern B Cybern. 2010 Jun;40(3):868-80. doi: 10.1109/TSMCB.2009.2031559. Epub 2009 Nov 13.
Manifold learning is a hot topic in the field of computer science, particularly since nonlinear dimensionality reduction based on manifold learning was proposed in Science in 2000. The work has achieved great success. The main purpose of current manifold-learning approaches is to search for independent intrinsic variables underlying high dimensional inputs which lie on a low dimensional manifold. In this paper, a new manifold is built up in the training step of the process, on which the input training samples are set to be close to each other if the values of their intrinsic variables are close to each other. Then, the process of dimensionality reduction is transformed into a procedure of preserving the continuity of the intrinsic variables. By utilizing the new manifold, the dynamic tracking of a human who can move and rotate freely is achieved. From the theoretical point of view, it is the first approach to transfer the manifold-learning framework to dynamic tracking. From the application point of view, a new and low dimensional feature for visual tracking is obtained and successfully applied to the real-time tracking of a free-moving object from a dynamic vision system. Experimental results from a dynamic tracking system which is mounted on a dynamic robot validate the effectiveness of the new algorithm.
流形学习是计算机科学领域的一个热门话题，特别是自2000年基于流形学习的非线性降维方法在《科学》杂志上被提出以来。这项工作取得了巨大的成功。当前流形学习方法的主要目的是寻找位于低维流形上的高维输入背后的独立内在变量。在本文中，在该过程的训练步骤中构建了一个新的流形，如果输入训练样本的内在变量值彼此接近，则在该流形上使它们彼此靠近。然后，降维过程被转化为一个保持内在变量连续性的过程。通过利用这个新的流形，实现了对可以自由移动和旋转的人的动态跟踪。从理论角度来看，这是将流形学习框架应用于动态跟踪的首次尝试。从应用角度来看，获得了一种用于视觉跟踪的新的低维特征，并成功应用于动态视觉系统中自由移动物体的实时跟踪。安装在动态机器人上的动态跟踪系统的实验结果验证了新算法的有效性。
