Estimate large motions using reliability-based dynamic programming

Abstract

Detecting and estimating motions of fast moving objects has many important applications. However, most existing motion estimation techniques have difficulties in handling large motions in the scene. In this paper, the reliability-based dynamic programming technique proposed by Gong and Yang is extended and applied to large motion estimation problem. Compared with the Gong and Yang approach, the extended algorithm removes the constant penalty assumption and also explicitly enforces the inter-scanline consistency constraint. The experimental results indicate that the new algorithm can effectively estimate velocities for fast moving objects. The algorithm can also be configured to produce sparse but reliable flow fields.