Enhancing Gradient Sparsity for Parametrized Motion Estimation

In this paper, we propose a novel motion estimation framework based on the sparsity associated with gradients of the parametrized motion field. Beginning with Shen and Wu’s sparse model for optic flow estimation [15], we show the sparsity of the motion field can be enhanced by increasing the degree of freedom of the parametrized motion model. With such an enhancement, we formulate the motion estimation as an `0 optimization problem. Along with an `1 norm regularization to the instant constancy assumption, this problem is solved by a reweighted `1 optimization approach. Experiments on constant, pure translational, and affine motion models certify that the enhanced sparsity provides improved accuracy for motion estimation.