Person Re-identification Using Clustering Ensemble Prototypes

Abstract

This paper presents an appearance-based model to deal with the person re-identification problem. Usually in a crowded scene, it is observed that, the appearances of most people are similar with regard to the combination of attire. In such situation it is a difficult task to distinguish an individual from a group of alike looking individuals and yields an ambiguity in recognition for re-identification. The proper organization of the individuals based on the appearance characteristics leads to recognize the target individual by comparing with a particular group of similar looking individuals. To reconstruct a group of individual according to their appearance is a crucial task for person re-identification. In this work we focus on unsupervised based clustering ensemble approach for discovering prototypes where each prototype represents similar set of gallery image instances. The formation of each prototype depends upon the appearance characteristics of gallery instances. The estimation of k-NN classifier is employed to specify a prototype to a given probe image. The similarity measure computation is performed between the probe and a subset of gallery images, that shares the same prototype with the probe and thus reduces the number of comparisons. Re-identification performance on benchmark datasets are presented using cumulative matching characteristic (CMC) curves.