A unified approach to background adaptation and initialization in public scenes
Pattern Recognition
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Methods of segmenting objects of interest from video data typically use a background model to represent an empty, static scene. However, dynamic processes in the background, such as moving foliage and water, can act to undermine the robustness of such methods and result in false positive object detections. Techniques for reducing errors have been proposed, including Markov Random Field (MRF) based pixel classification schemes, and also the use of region-based models. The work we present here combines these two approaches, using a region-based background model to provide robust likelihoods for multi-class MRF pixel labelling. Our initial results show the effectiveness of our method, by comparing performance with an analogous per-pixel likelihood model.