{
  "id": "1608.06197",
  "version": "v1",
  "published": "2016-08-22T15:43:29.000Z",
  "updated": "2016-08-22T15:43:29.000Z",
  "title": "CrowdNet: A Deep Convolutional Network for Dense Crowd Counting",
  "authors": [
    "Lokesh Boominathan",
    "Srinivas S S Kruthiventi",
    "R. Venkatesh Babu"
  ],
  "comment": "Accepted at ACM Multimedia (MM) 2016",
  "categories": [
    "cs.CV"
  ],
  "abstract": "Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds. We use a combination of deep and shallow, fully convolutional networks to predict the density map for a given crowd image. Such a combination is used for effectively capturing both the high-level semantic information (face/body detectors) and the low-level features (blob detectors), that are necessary for crowd counting under large scale variations. As most crowd datasets have limited training samples (<100 images) and deep learning based approaches require large amounts of training data, we perform multi-scale data augmentation. Augmenting the training samples in such a manner helps in guiding the CNN to learn scale invariant representations. Our method is tested on the challenging UCF_CC_50 dataset, and shown to outperform the state of the art methods.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-08-22T15:43:29.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep convolutional network",
      "dense crowd counting",
      "learn scale invariant representations",
      "perform multi-scale data augmentation",
      "high-level semantic information"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}