Parallel distributed detection of feature trajectories in multiple discontinuous motion image sequences

This paper is concerned with three-dimensional interpretation of image sequences showing multiple objects in motion. Each object exhibits smooth motion except at certain time instants when a motion discontinuity may occur. The objects are assumed to contain point features which are detected as the images are acquired. Estimating feature trajectories in the first two frames amounts to feature matching. As more images are acquired, existing trajectories are extended. Both initial detection and extension of trajectories are done by enforcing pertinent constraints from among the following: similarity of image plane arrangement of neighboring features, smoothness of three dimensional motion, and smoothness of image plane motion. The constraints are incorporated into energy functions which are minimized using two-dimensional Hopfield networks. Wrong matches that result from convergence to local minima are eliminated using a one-dimensional Hopfield like network. Experimental results on several image sequences are shown.