{
  "id": "2407.14620",
  "version": "v1",
  "published": "2024-07-19T18:28:13.000Z",
  "updated": "2024-07-19T18:28:13.000Z",
  "title": "The Research of Group Re-identification from Multiple Cameras",
  "authors": [
    "Hao Xiao"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Object re-identification is of increasing importance in visual surveillance. Most existing works focus on re-identify individual from multiple cameras while the application of group re-identification (Re-ID) is rarely discussed. We redefine Group Re-identification as a process which includes pedestrian detection, feature extraction, graph model construction, and graph matching. Group re-identification is very challenging since it is not only interfered by view-point and human pose variations in the traditional re-identification tasks, but also suffered from the challenges in group layout change and group member variation. To address the above challenges, this paper introduces a novel approach which leverages the multi-granularity information inside groups to facilitate group re-identification. We first introduce a multi-granularity Re-ID process, which derives features for multi-granularity objects (people/people-subgroups) in a group and iteratively evaluates their importances during group Re-ID, so as to handle group-wise misalignments due to viewpoint change and group dynamics. We further introduce a multi-order matching scheme. It adaptively selects representative people/people-subgroups in each group and integrates the multi-granularity information from these people/people-subgroups to obtain group-wise matching, hence achieving a more reliable matching score between groups. Experimental results on various datasets demonstrate the effectiveness of our approach.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-07-19T18:28:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "multiple cameras",
      "multi-granularity information inside groups",
      "group layout change",
      "group member variation",
      "human pose variations"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}