{
  "id": "1703.09771",
  "version": "v1",
  "published": "2017-03-28T19:55:19.000Z",
  "updated": "2017-03-28T19:55:19.000Z",
  "title": "Deep 6-DOF Tracking",
  "authors": [
    "Mathieu Garon",
    "Jean-François Lalonde"
  ],
  "comment": "8 pages, 7 figures",
  "categories": [
    "cs.CV"
  ],
  "abstract": "We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture. Our method is both more accurate and more robust to occlusions than the existing best performing approaches while maintaining real-time performance. To assess its efficacy, we evaluate our approach on several challenging RGBD sequences of real objects in a variety of conditions. Notably, we systematically evaluate robustness to occlusions through a series of sequences where the object to be tracked is increasingly occluded. Finally, our approach is purely data-driven and does not require any hand-designed features: robust tracking is automatically learned from data.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-03-28T19:55:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "achieve state-of-the-art performance",
      "real world capture",
      "systematically evaluate robustness",
      "leverages deep",
      "real objects"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 8,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}