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Non-rigid structure from motion using ranklet-based tracking and non-linear optimization

  1. Del Bue, F. Smeraldi and L. Agapito, in: Image and Vision Computing, volume 25, issue 3, pages 297-310, March 2007


In this paper, we address the problem of estimating the 3D structure and motion of a deformable object given a set of image features tracked automatically throughout a video sequence. Our contributions are twofold: firstly, we propose a new approach to improve motion and structure estimates using a non-linear optimization scheme and secondly we propose a tracking algorithm based on ranklets, a recently developed family of orientation selective rank features. It has been shown that if the 3D deformations of an object can be modeled as a linear combination of shape bases then both its motion and shape may be recovered using an extension of Tomasi and Kanade's factorization algorithm for affine cameras. Crucially, these new factorization methods are model free and work purely from video in an unconstrained case: a single uncalibrated camera viewing an arbitrary 3D surface which is moving and articulating. The main drawback of existing methods is that they do not provide correct structure and motion estimates: the motion matrix has a repetitive structure which is not respected by the factorization algorithm. In this paper, we present a non-linear optimization method to refine the motion and shape estimates which minimizes the image reprojection error and imposes the correct structure onto the motion matrix by choosing an appropriate parameterization. Factorization algorithms require as input a set of feature tracks or correspondences found throughout the image sequence. The challenge here is to track the features while the object is deforming and the appearance of the image therefore changing. We propose a model free tracking algorithm based on ranklets, a multi-scale family of rank features that present an orientation selectivity pattern similar to Haar wavelets. A vector of ranklets is used to encode an appearance based description of a neighborhood of each tracked point. Robustness is enhanced by adapting, for each point, the shape of the filters to the structure of the particular neighborhood. A stack of models is maintained for each tracked point in order to manage large appearance variations with limited drift. Our experiments on sequences of a human subject performing different facial expressions show that this tracker provides a good set of feature correspondences for the non-rigid 3D reconstruction algorithm.

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