3-D MOTION FROM IMAGE SEQUENCES: MODELING, UNDERSTANDING AND PREDICTION.

An approach to understanding general 3-D motion of a rigid body from image sequences is presented. 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. The trajectory of the rotation center is approximated by a vector polynomial. The nature and parameters of short-term motion can be estimated continuously with the goal of understanding motion through the image sequence. Noise smoothing is achieved by overdetermination and a least-squares criterion. 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. Simulation results are given for both noisy synthetic data and images taken of a model airplane.