Interactive Fuzzy Cellular Automata for Fast Person Re-Identification

Abstract

One of the goals of person re-identification systems is to support video-surveillance operators and forensic investigators to find an individual of interest in videos acquired by a network of non-overlapping cameras. This is attained by sorting images of previously observed individuals for decreasing values of their similarity with a given probe individual. Existing appearance descriptors, together with their similarity measures, are mostly aimed at improving ranking quality. We propose two fuzzy-based descriptors which are fast in terms of the processing time on descriptor generation and matching score computation. We then evaluate our approach on three benchmark data sets (VIPeR, i-LIDS, and ETHZ) with comparison of some descriptors in the state-of-the-art.