Learning from Synthetic Data for Crowd Counting in the Wild

Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). It is a difficult task in the wild: changeable environment, large-range number of people cause the current methods can not work well. In addition, due to the scarce data, many methods suffer from over-fitting to a different extent. To remedy the above two problems, firstly, we develop a data collector and labeler, which can generate the synthetic crowd scenes and simultaneously annotate them without any manpower. Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild.

Released in: Learning from Synthetic Data for Crowd Counting in the Wild

Source: arXiv - Learning from Synthetic Data for Crowd Counting in the Wild

Contributor:

by

Summary

Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). It is a difficult task in the wild: changeable environment, large-range number of people cause the current methods can not work well. In addition, due to the scarce data, many methods suffer from over-fitting to a different extent. To remedy the above two problems, firstly, we develop a data collector and labeler, which can generate the synthetic crowd scenes and simultaneously annotate them without any manpower. Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild.

15,212

Images in dataset

2018

Year Released

Key Links & Stats

@inproceedings{wang2019learning, title={Learning from Synthetic Data for Crowd Counting in the Wild}, author={Wang, Qi and Gao, Junyu and Lin, Wei and Yuan, Yuan}, booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, pages={8198--8207}, year={2019} }

scenebox

Modalities

  1. Video

Verticals

  1. A/V

ML Task

  1. Object Detection

Related organizations