{
  "id": "1806.10287",
  "version": "v1",
  "published": "2018-06-27T03:52:19.000Z",
  "updated": "2018-06-27T03:52:19.000Z",
  "title": "Attention to Head Locations for Crowd Counting",
  "authors": [
    "Youmei Zhang",
    "Chunluan Zhou",
    "Faliang Chang",
    "Alex C. Kot"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Occlusions, complex backgrounds, scale variations and non-uniform distributions present great challenges for crowd counting in practical applications. In this paper, we propose a novel method using an attention model to exploit head locations which are the most important cue for crowd counting. The attention model estimates a probability map in which high probabilities indicate locations where heads are likely to be present. The estimated probability map is used to suppress non-head regions in feature maps from several multi-scale feature extraction branches of a convolution neural network for crowd density estimation, which makes our method robust to complex backgrounds, scale variations and non-uniform distributions. In addition, we introduce a relative deviation loss to compensate a commonly used training loss, Euclidean distance, to improve the accuracy of sparse crowd density estimation. Experiments on Shanghai-Tech, UCF_CC_50 and World-Expo'10 data sets demonstrate the effectiveness of our method.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-06-27T03:52:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "crowd counting",
      "head locations",
      "world-expo10 data sets demonstrate",
      "attention model",
      "complex backgrounds"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}