Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801.
IEEE Trans Pattern Anal Mach Intell. 1987 Mar;9(3):370-89. doi: 10.1109/tpami.1987.4767920.
This paper presents an approach to understanding general 3-D motion of a rigid body from image sequences. Based on dynamics, a locally constant angular momentum (LCAM) model is introduced. The model is local in the sense that it is applied to a limited number of image frames at a time. Specifically, the model constrains the motion, over a local frame subsequence, to be a superposition of precession and translation. Thus, the instantaneous rotation axis of the object is allowed to change through the subsequence. The trajectory of the rotation center is approximated by a vector polynomial. The parameters of the model evolve in time so that they can adapt to long term changes in motion characteristics. The nature and parameters of short term motion can be estimated continuously with the goal of understanding motion through the image sequence. The estimation algorithm presented in this paper is linear, i.e., the algorithm consists of solving simultaneous linear equations. Based on the assumption that the motion is smooth, object positions and motion in the near future can be predicted, and short missing subsequences can be recovered. Noise smoothing is achieved by overdetermination and a leastsquares criterion. The framework is flexible in the sense that it allows both overdetermination in number of feature points and the number of image frames.
本文提出了一种从图像序列中理解刚体一般三维运动的方法。基于动力学，引入了局部角动量（LCAM）模型。该模型是局部的，因为它一次应用于有限数量的图像帧。具体来说，该模型约束运动，在局部帧子序列上，是进动和平移的叠加。因此，物体的瞬时旋转轴可以在子序列中改变。物体旋转中心的轨迹通过矢量多项式进行近似。模型的参数随时间演变，以便它们能够适应运动特征的长期变化。可以通过理解图像序列中的运动来连续估计短期运动的性质和参数。本文提出的估计算法是线性的，即算法由求解联立线性方程组成。基于运动是平滑的假设，可以预测物体的位置和未来的运动，并恢复短的缺失子序列。通过超定和最小二乘准则实现噪声平滑。该框架是灵活的，因为它允许特征点的数量和图像帧的数量超定。
