Tracking Multiple People with Illumination Maps

We address the problem of multiple people tracking under non-homogenous and time-varying illumination conditions. We propose a unified framework for jointly estimating the position of the targets and their illumination conditions. For each target multiple templates are considered to model appearance variations due to lighting changes. The template choice is driven by an illumination map which describes the light conditions in different areas of the scene. This map is computed with a novel algorithm for efficient inference in a hierarchical Markov Random Field (MRF) and is updated online to adapt to slow lighting changes. Experimental results demonstrate the effectiveness of our approach.