This will lock onto the motion of a single region in the image even in the presense of many image motions. Most existing algorithms either assume motion segmentation, or cannot handle multiple motions. The object recognition process will use qualitative features and invariant descriptions, which are stable along the image sequences. Their computation will be done directly from 2D image motion. These direct algorithms are designed to be used by a system with limited space and computation resources.
Most existing algorithms seek a complete depth map representation, which is harder to compute.
Robust statistics will be used in feature computation. The simplifying, and mostly incorrect, assumption of Gaussian noise is not necessary. Model matching is based on an iterative, accumulative, Kalman filter. This is the main point in which interaction with the system is allowed, in order for the user to be able to define constraints among the detected features. Good results were obtained in motion segmantation, and in computing camera motion in a static scene.