{
  "id": "1706.00553",
  "version": "v1",
  "published": "2017-06-02T04:34:08.000Z",
  "updated": "2017-06-02T04:34:08.000Z",
  "title": "Rank Persistence: Assessing the Temporal Performance of Real-World Person Re-Identification",
  "authors": [
    "Srikrishna Karanam",
    "Eric Lam",
    "Richard J. Radke"
  ],
  "comment": "8 pages, 7 figures",
  "categories": [
    "cs.CV"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-06-02T04:34:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "real-world person re-identification",
      "temporal aspect",
      "multiple candidate reappearances",
      "temporal performance characteristics",
      "designing useful person re-identification systems"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 8,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}