{
  "id": "1804.06958",
  "version": "v1",
  "published": "2018-04-19T01:01:16.000Z",
  "updated": "2018-04-19T01:01:16.000Z",
  "title": "A-cCCNN: adaptive ccnn for density estimation and crowd counting",
  "authors": [
    "Saeed Amirgholipour Kasmani",
    "Xiangjian He",
    "Wenjing Jia",
    "Dadong Wang",
    "Michelle Zeibots"
  ],
  "comment": "5 pages, 2 figures, ICIP conference",
  "categories": [
    "cs.CV"
  ],
  "abstract": "Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community. Although many attempts have been reported, real-world problems, such as huge variation in subjects' sizes in images and serious occlusion among people, make it still a challenging problem. In this paper, we propose an Adaptive Counting Convolutional Neural Network (A-CCNN) and consider the scale variation of objects in a frame adaptively so as to improve the accuracy of counting. Our method takes advantages of contextual information to provide more accurate and adaptive density maps and crowd counting in a scene. Extensively experimental evaluation is conducted using different benchmark datasets for object-counting and shows that the proposed approach is effective and outperforms state-of-the-art approaches.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-04-19T01:01:16.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "crowd counting",
      "density estimation",
      "adaptive ccnn",
      "outperforms state-of-the-art approaches",
      "adaptive counting convolutional neural network"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}