Exploiting Dissimilarity Representations for Person Re-Identification

Person re-identification is the task of recognizing an individual that has already been observed over a network of video-surveillance cameras. Methods proposed in literature so far addressed this issue as a classical matching problem: a descriptor is built directly from the view of the person, and a similarity measure between descriptors is defined accordingly. In this work, we propose a general dissimilarity framework for person re-identification, aimed at transposing a generic method for person re-identification based to the commonly adopted multiple instance representation, into a dissimilarity form. Individuals are thus represented by means of dissimilarity values, in respect to common prototypes. Dissimilarity representations carry appealing advantages, in particular the compactness of the resulting descriptor, and the extremely low time required to match two descriptors. Moreover, a dissimilarity representation enables various new applications, some of which are depicted in the paper. An experimental evaluation of the proposed framework applied to an existing method is provided, which clearly shows the advantages of dissimilarity representations in the context of person re-identification.