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

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Computer Vision – ECCV 2020 Workshops (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12538))

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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\(^1\). Moreover, a comprehensive ablation study is conducted to explore each component’s contribution.

Z. Zhao and T. Han—Equal Contribution.

\(^1\) Finally, reach the MAE of 12.36, ranked the second.

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Authors and Affiliations

  1. School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, People’s Republic of China

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

Authors
  1. Zhiyuan Zhao
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  2. Tao Han
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  3. Junyu Gao
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  4. Qi Wang
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  5. Xuelong Li
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Corresponding author

Correspondence to Qi Wang .

Editor information

Editors and Affiliations

  1. University of Clermont Auvergne, Clermont Ferrand, France

    Adrien Bartoli

  2. Università degli Studi di Udine, Udine, Italy

    Andrea Fusiello

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Zhao, Z., Han, T., Gao, J., Wang, Q., Li, X. (2020). A Flow Base Bi-path Network for Cross-Scene Video Crowd Understanding in Aerial View. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12538. Springer, Cham. https://doi.org/10.1007/978-3-030-66823-5_34

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  • DOI: https://doi.org/10.1007/978-3-030-66823-5_34

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  • Online ISBN: 978-3-030-66823-5

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