Rank Persistence: Assessing the Temporal Performance of Real-World Person Re-Identification
This addresses the temporal performance gap in person re-identification for real-world operators, though it is incremental as it focuses on evaluation rather than new algorithms.
The paper tackles the problem of assessing person re-identification performance over time in real-world scenarios, proposing a Rank Persistence Curve (RPC) to measure how long correct matches remain in a rank-k shortlist, and demonstrates it using a new long-term dataset.
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.