Bayesian Illumination-Invariant Motion Detection

T. Aach, L. Dümbgen, R. Mester, D. Toth


We describe an algorithm for change detection which is insensitive to both slow and fast temporal variations of scene illumination. Our algorithm is based on statistical decision theory by using a Bayesian approach. The goal is to detect only temporal changes which are induced by true scene changes, like motion, but not changes due to varying illumination or noise. To this end, our algorithm uses a simple illumination model which is invariant to common camera nonlinearities like gamma-nonlinearity. This is combined with a model for the influence of noise as well as an a priori model for the expected properties of the sought change masks. Key ingredients of the resulting algorithm are a suitable test statistic and an adaptive threshold mechanism. As the algorithm can be applied in a noniterative manner, it is also computationally attractive.

Original languageEnglish
Number of pages4
Publication statusPublished - 01.01.2001
EventIEEE International Conference on Image Processing 2001 - Thessaloniki, Greece
Duration: 07.10.200110.10.2001
Conference number: 58802


ConferenceIEEE International Conference on Image Processing 2001
Abbreviated titleICIP 2001


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