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CountFormer: Multi-view Crowd Counting Transformer

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Multi-view counting (MVC) methods have shown their superiority over single-view counterparts, particularly in situations characterized by heavy occlusion and severe perspective distortions. However, hand-crafted heuristic features and identical camera layout requirements in conventional MVC methods limit their applicability and scalability in real-world scenarios. In this work, we propose a concise 3D MVC framework called CountFormer to elevate multi-view image-level features to a scene-level volume representation and estimate the 3D density map based on the volume features. By incorporating a camera encoding strategy, CountFormer successfully embeds camera parameters into the volume query and image-level features, enabling it to handle various camera layouts with significant differences. Furthermore, we introduce a feature lifting module capitalized on the attention mechanism to transform image-level features into a 3D volume representation for each camera view. Subsequently, the multi-view volume aggregation module attentively aggregates various multi-view volumes to create a comprehensive scene-level volume representation, allowing CountFormer to handle images captured by arbitrary dynamic camera layouts. The proposed method performs favorably against the state-of-the-art approaches across various widely used datasets, demonstrating its greater suitability for real-world deployment compared to conventional MVC frameworks.

H. Mo and X. Zhang—Equal contributions.

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Acknowledgements

The invaluable support and sponsorship provided by the Open Project Program of the State Key Laboratory of Virtual Reality Technology and Systems at Beihang University (Project No. VRLAB2024C05) has been instrumental to the successful completion of this research endeavor. The financial resources and institutional backing afforded by this esteemed program have been pivotal in enabling the pursuit and realization of the scholarly insights presented herein. It is with the utmost gratitude that the author acknowledges the crucial role played by this prestigious source of support in empowering the investigative work underlying this scholarly contribution.

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Authors and Affiliations

  1. Hubei University of Arts and Science, Wuhan, China

    Hong Mo, Qiong Gu & Bo Hang

  2. Neolix Autonomous Vehicle, Guangzhou, China

    Xiong Zhang & Cheng Yang

  3. Kuaishou Technology, Beijing, China

    Jianchao Tan

  4. Sun Yat-Sen University, Guangzhou, China

    Wenqi Ren

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  1. Hong Mo
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Correspondence to Hong Mo .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Mo, H. et al. (2025). CountFormer: Multi-view Crowd Counting Transformer. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15110. Springer, Cham. https://doi.org/10.1007/978-3-031-72943-0_2

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