Dense Linear-Time Correspondences for Tracking

Abstract

A novel method is proposed for the problem of frame-to-frame correspondence search in video sequences. The method, based on hashing of low-dimensional image descriptors, establishes dense correspondences and allows large motions. All image pixels are considered for matching, the notion of interest points is reviewed. In our formulation, points of interest are those that can be reliably matched. Their saliency depends on properties of the chosen matching function and on actual image content. Both computational time and memory requirements of the correspondence search are asymptotically linear in the number of image pixels, irrespective of correspondence density and of image content. All steps of the method are simple and allow for a hardware implementation. Functionality is demonstrated on sequences taken from a vehicle moving in an urban environment.