A multi-stage approach for fast person re-identification

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 taken by a network of non-overlapping cameras. This is attained by sorting images of previously observed individuals for decreasing values of their similarity with the query individual. Several appearance-based descriptors have been proposed so far, together with ad hoc similarity measures, mostly aimed at improving ranking quality. We address instead the issue of the processing time required to compute the similarity values, and propose a multi-stage ranking approach to attain a trade-off with ranking quality, for any given descriptor. We give a preliminary evaluation of our approach on the benchmark VIPeR data set, using different state-of-the-art descriptors.