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July 2020

tl;dr: A large scale (15k training images) dataset for crowded/dense human detection.

Overall impression

Very solid technical report from megvii (face++). Related datasets: WiderPerson.

Previous datasets are more likely to annotate crowd human as a whole ignored region, which cannot be counted as valid samples in training and evaluation.

Key ideas

  • 22 human per image.
  • Full body bbox (amodal), visible bbox (only visible region), head bbox. They are bound (associated) for each human instance.
  • occlusion ratio can be quantified by the two bbox.
  • evaluation metric:
    • AP
    • mMR (average log miss rate over FP per image)

Technical details

  • Image crawled from google image search engine, cleaned and annotated.
  • Pervious datasets (CityPerson) annotates top of the head to the middle of the feet and generated a full bbox with fixed aspect ratio of 0.41.

Notes