Person Re-identification Using the Silhouette Shape Described by a Point Distribution Model

Abstract

In this paper, we present a new shape-based system for person re-identification. The silhouette shape is represented by a Point Distribution Model (PDM) aligned on the body. We improve a fitting model which iteratively adjusts the shape by maximizing a boosted score of local features: the "Boosted Deformable Model". We modify the training procedure with a ranking structure to find how the model can approach the correct fitting. This is enhanced by the use of weak Artificial Neural Networks as regression functions. Then, we experiment the use of two kind of descriptors on the aligned model : a pose shape signature built with the Shape Context on the set of landmarks and an appearance-based signature using color histograms on the warped appearance contained in the shape model. We demonstrate our approach with evaluations employing the alignment and re-identification modules. The results show that our improvements provide a more accurate fitting, the adapted shape representation has a potential discriminant for re-identification through the pose and employing a PDM instead of a pixel mask to describe silhouette enhances performance of conventional appearance features.