Illumination Invariant Robust Likelihood Estimator for Particle Filtering Based Target Tracking

Tracking visual targets under illumination changes is a challenging problem, especially when the illumination varies across different regions of the target. In this paper, we solve the problem of illumination invariant tracking during likelihood estimation within the particle filter. Existing particle filter based tracking frameworks mainly deal with changes in illumination by the choice of color-space or features. This paper presents an alternate likelihood estimation algorithm that helps dealing with illumination changes using a homomogrphic filtering based weighted illumination model. That is, a homomorphic filter is first used to separate the illumination and reflectance components from the image, and further by associating an appropriate weight to the illumination, the target image is reconstructed for the accurately measuring the likelihood. The proposed algorithm is implemented using a simple particle filter tracking framework and compared against other tracking algorithms on scenarios with large illumination variations.