{
  "id": "1910.12273",
  "version": "v1",
  "published": "2019-10-27T14:46:08.000Z",
  "updated": "2019-10-27T14:46:08.000Z",
  "title": "Exploring 3 R's of Long-term Tracking: Re-detection, Recovery and Reliability",
  "authors": [
    "Shyamgopal Karthik",
    "Abhinav Moudgil",
    "Vineet Gandhi"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Recent works have proposed several long term tracking benchmarks and highlight the importance of moving towards long-duration tracking to bridge the gap with application requirements. The current evaluation methodologies, however, do not focus on several aspects that are crucial in a long term perspective like Re-detection, Recovery, and Reliability. In this paper, we propose novel evaluation strategies for a more in-depth analysis of trackers from a long-term perspective. More specifically, (a) we test re-detection capability of the trackers in the wild by simulating virtual cuts, (b) we investigate the role of chance in the recovery of tracker after failure and (c) we propose a novel metric allowing visual inference on the ability of a tracker to track contiguously (without any failure) at a given accuracy. We present several original insights derived from an extensive set of quantitative and qualitative experiments.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-10-27T14:46:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "long-term tracking",
      "reliability",
      "long term tracking benchmarks",
      "test re-detection capability",
      "current evaluation methodologies"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}