Abstract
People tracking under non-uniform illumination is challenging, as observed appearance may change as they move around in the environment. Appearance model adaptation is inconvenient over the long run as it is subject to drift, while filtering illumination information in the data through built-in invariance is sub-optimal in terms of discriminative capability. In this work, we are interested in modeling the spatial and temporal dimensions of appearance variation induced by non-uniform illumination, and to learn and adapt related parameters over time by using walking people as illumination probes. We propose a hybrid graphical model and a new message passing scheme that sequentially updates parameters of the model, so that scene illumination can be learnt online and used for robust tracking in dynamic environment. © 2013 Springer-Verlag.