Multi-Stage Ranking 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 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 address instead the issue of processing time, which is also relevant in practical applications involving interaction with human operators. We show how a trade-off between processing time and ranking quality, emph{for any given descriptor}, can be achieved through a multi-stage ranking approach inspired by multi-stage classification approaches, which we adapt to the re-identification ranking task. We analytically model the processing time of multi-stage system and discuss the corresponding accuracy, and derive from these results practical design guidelines. We then emprically evaluate our approach on three benchmark data sets and four state-of-the-art descriptors.