{
  "id": "1602.07125",
  "version": "v1",
  "published": "2016-02-23T11:34:58.000Z",
  "updated": "2016-02-23T11:34:58.000Z",
  "title": "Car Type Recognition with Deep Neural Networks",
  "authors": [
    "Heikki Huttunen",
    "Fatemeh Shokrollahi Yancheshmeh",
    "Ke Chen"
  ],
  "comment": "Submitted to IEEE Intelligent Vehicles Symposium 2016",
  "categories": [
    "cs.CV"
  ],
  "abstract": "In this paper we study automatic recognition of cars of four types: Bus, Truck, Van and Small car. For this problem we consider two data driven frameworks: a deep neural network and a support vector machine using SIFT features. The accuracy of the methods is validated with a database of over 6500 images, and the resulting prediction accuracy is over 97 %. This clearly exceeds the accuracies of earlier studies that use manually engineered feature extraction pipelines.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-02-23T11:34:58.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep neural network",
      "car type recognition",
      "manually engineered feature extraction pipelines",
      "study automatic recognition",
      "data driven frameworks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2016arXiv160207125H"
    }
  }
}