{
  "id": "1705.10716",
  "version": "v1",
  "published": "2017-05-30T16:11:34.000Z",
  "updated": "2017-05-30T16:11:34.000Z",
  "title": "Addressing Ambiguity in Multi-target Tracking by Hierarchical Strategy",
  "authors": [
    "Ali Taalimi",
    "Liu Liu",
    "Hairong Qi"
  ],
  "comment": "5 pages, Accepted in International Conference of Image Processing, 2017",
  "categories": [
    "cs.CV"
  ],
  "abstract": "This paper presents a novel hierarchical approach for the simultaneous tracking of multiple targets in a video. We use a network flow approach to link detections in low-level and tracklets in high-level. At each step of the hierarchy, the confidence of candidates is measured by using a new scoring system, ConfRank, that considers the quality and the quantity of its neighborhood. The output of the first stage is a collection of safe tracklets and unlinked high-confidence detections. For each individual detection, we determine if it belongs to an existing or is a new tracklet. We show the effect of our framework to recover missed detections and reduce switch identity. The proposed tracker is referred to as TVOD for multi-target tracking using the visual tracker and generic object detector. We achieve competitive results with lower identity switches on several datasets comparing to state-of-the-art.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-05-30T16:11:34.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "multi-target tracking",
      "hierarchical strategy",
      "addressing ambiguity",
      "network flow approach",
      "lower identity switches"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}