Rank Persistence: Assessing the Temporal Performance of Real-World Person Re-Identification

TL;DR

This paper introduces Rank Persistence Curves (RPCs) to evaluate how long correct person re-identification matches remain in top-k lists over time, addressing real-world temporal challenges.

Contribution

It proposes a novel metric, RPC, for assessing the temporal stability of re-identification systems in dynamic, long-term scenarios.

Findings

RPC effectively compares temporal performance of algorithms

Long-term dataset reveals re-identification challenges over time

Temporal performance varies significantly among algorithms

Abstract

Designing useful person re-identification systems for real-world applications requires attention to operational aspects not typically considered in academic research. Here, we focus on the temporal aspect of re-identification; that is, instead of finding a match to a probe person of interest in a fixed candidate gallery, we consider the more realistic scenario in which the gallery is continuously populated by new candidates over a long time period. A key question of interest for an operator of such a system is: how long is a correct match to a probe likely to remain in a rank-k shortlist of possible candidates? We propose to distill this information into a Rank Persistence Curve (RPC), which allows different algorithms' temporal performance characteristics to be directly compared. We present examples to illustrate the RPC using a new long-term dataset with multiple candidate reappearances, and discuss considerations for future re-identification research that explicitly involves temporal aspects.