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Nonrigid motion tracking of image sequences based on smoothness constraints

To estimate the motion and deformation of nonrigid objects accurately from image sequences, this paper proposes a method for nonrigid motion tracking based on smoothness constraints, which have an effect on noise reduction in optical flow computation by a gradient-based method. The motion of nonrigid objects generally is assumed to be smooth according to such physical properties as inertia and elasticity. Therefore, such smoothness constraints are defined in space (in frame) and time (between frames) as an active net model in formulating an energy functional and Lagrange equation, and the optical flow field derived from image sequences by shape preservation and natural deformation is modified. In addition, applying continuous Kalman filtering theory for long sequences of image frames, it is possible to estimate the temporal continuous deformation of elastic objects. The nonrigid motion tracking is demonstrated by experiments involving the modification of the shape distortion yielded by noise and the reconstruction of the real motion and deformation.