On the design of filters for gradient-based motion estimation
Gradient based approaches in motion estimation (Optical-Flow) refer to those techniques that estimate the motion of an image sequence based on local derivatives in the image intensity. In order to best evaluate local changes, specific filters are applied to the image sequence. These filters are typically composed of a spatiotemporal pre-smoothing filter followed by discrete derivative ones. The design of these filters plays an important role in the estimation accuracy. This paper proposes a method for such a design. Unlike previous methods that consider these filters as optimized approximations for continuum derivatives, the proposed design procedure defines the optimality directly with respect to the motion estimation goal. One possible result of the suggested scheme is a set of image dependent filters that can be computed prior to the estimation process. An alternative interpretation is the creation of generic filters, capable of treating natural images. Simulations demonstrate the validity of the new design approach.
motion estimation pre-smoothing optical flow gradients computation optimal filters constrained minimization