Efficient planar object tracking and parameter estimation using compactly represented cubic B-Spline curves

In this paper, we consider the problem of matching tno-dimensional (2-D) planar object curves from a database, and tracking moving object curves through an image sequence, The first part of the paper describes a curve data compression method using B-spline curve approximation. We present a new constrained active B-spline curie model based on the minimum mean square error (MMSE) criterion, and an iterative algorithm for selecting the "best" segment border points for each B-spline curve, The second part of the paper describes a method for simultaneous object tracking and affine parameter estimation using the approximate curves and profiles, We propose a novel B-spline point assignment algorithm which incorporates the significant corners for interpolating corresponding paints on the two curves to be compared. A gradient-based algorithm is presented for simultaneously tracking object curl es, and estimating the associated translation, rotation and scaling parameters. The performance of each proposed method is evaluated using still images and image sequences containing simple objects.