A Flow Base Bi-Path Network for Cross-Scene Video Crowd Understanding in Aerial View

Zhiyuan Zhao, Tao Han, Junyu Gao, Qi Wang, Xuelong Li

ECCVW 2020 pp. 574-587

doi:10.1007/978-3-030-66823-5_34 /eccvw/2020/zhao2020eccvw-flow/

Abstract

Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks. The variability of the shooting location adds some intractable challenges to these missions, such as varying scale, unstable exposure, and scene migration. In this paper, we strive to tackle the above challenges and automatically understand the crowd from the visual data collected from drones. First, to alleviate the background noise generated in cross-scene testing, a double-stream crowd counting model is proposed, which extracts optical flow and frame difference information as an additional branch. Besides, to improve the model's generalization ability at different scales and time, we randomly combine a variety of data transformation methods to simulate some unseen environments. To tackle the crowd density estimation problem under extreme dark environments, we introduce synthetic data generated by game Grand Theft Auto V(GTAV). Experiment results show the effectiveness of the virtual data. Our method wins the challenge with a mean absolute error (MAE) of 12.70. Moreover, a comprehensive ablation study is conducted to explore each component's contribution.

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Cite

Text

Zhao et al. "A Flow Base Bi-Path Network for Cross-Scene Video Crowd Understanding in Aerial View." European Conference on Computer Vision Workshops, 2020. doi:10.1007/978-3-030-66823-5_34

Markdown

[Zhao et al. "A Flow Base Bi-Path Network for Cross-Scene Video Crowd Understanding in Aerial View." European Conference on Computer Vision Workshops, 2020.](https://mlanthology.org/eccvw/2020/zhao2020eccvw-flow/) doi:10.1007/978-3-030-66823-5_34

BibTeX

@inproceedings{zhao2020eccvw-flow,
  title     = {{A Flow Base Bi-Path Network for Cross-Scene Video Crowd Understanding in Aerial View}},
  author    = {Zhao, Zhiyuan and Han, Tao and Gao, Junyu and Wang, Qi and Li, Xuelong},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2020},
  pages     = {574-587},
  doi       = {10.1007/978-3-030-66823-5_34},
  url       = {https://mlanthology.org/eccvw/2020/zhao2020eccvw-flow/}
}