Robust selection of parametric motion models in image sequences

Abstract

Parametric motion models are commonly used in image sequence analysis for different tasks. A robust estimation framework is usually required to reliably compute the motion model. The choice of the right model is also important. However, dealing simultaneously with both issues remains an open question. We propose a robust motion model selection method with two variants, which relies on the Fisher test. We also derive an interpretation of it as a robust Mallows' CP criterion. The resulting criterion is straightforward to compute. We have conducted a comparative experimental evaluation on different image sequences demonstrating the interest and the efficiency of the proposed method.