People re-identification in camera networks based on probabilistic color histograms

People tracking has to face many issues in video surveillance scenarios. One of the most challenging aspect is to re-identify people across different cameras. Humans, indeed, change appearance according to pose, clothes and illumination conditions and thus defining features that are able to robustly describe people moving in a camera network is a not trivial task. While color is widely exploited in the distinction and recognition of objects, most of the color descriptors proposed so far are not robust in complex applications such as video surveillance scenarios. A new color based feature is introduced in this paper to describe the color appearance of the subjects. For each target a probabilistic color histogram (PCH) is built by using a fuzzy K-Nearest Neighbors (KNN) classifier trained on an ad-hoc dataset and is used to match two corresponding appearances of the same person in different cameras of the network. The experimental results show that the defined descriptor is effective at discriminating and re-identifying people across two different video cameras regardless of the viewpoint change between the two views and outperforms state of the art appearance based techniques.