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Calibration-Free Multi-view Crowd Counting

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13669))

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Abstract

Deep learning based multi-view crowd counting (MVCC) has been proposed to handle scenes with large size, in irregular shape or with severe occlusions. The current MVCC methods require camera calibrations in both training and testing, limiting the real application scenarios of MVCC. To extend and apply MVCC to more practical situations, in this paper we propose calibration-free multi-view crowd counting (CF-MVCC), which obtains the scene-level count directly from the density map predictions for each camera view without needing the camera calibrations in the test. Specifically, the proposed CF-MVCC method first estimates the homography matrix to align each pair of camera-views, and then estimates a matching probability map for each camera-view pair. Based on the matching maps of all camera-view pairs, a weight map for each camera view is predicted, which represents how many cameras can reliably see a given pixel in the camera view. Finally, using the weight maps, the total scene-level count is obtained as a simple weighted sum of the density maps for the camera views. Experiments are conducted on several multi-view counting datasets, and promising performance is achieved compared to calibrated MVCC methods that require camera calibrations as input and use scene-level density maps as supervision.

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Acknowledgements

This work was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 11212518, CityU 11215820), and by a Strategic Research Grant from City University of Hong Kong (Project No. 7005665).

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Authors and Affiliations

  1. College of Computer Science & Software Engineering, Shenzhen University, Shenzhen, China

    Qi Zhang

  2. Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China

    Qi Zhang & Antoni B. Chan

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  1. Qi Zhang
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  2. Antoni B. Chan
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Correspondence to Qi Zhang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Zhang, Q., Chan, A.B. (2022). Calibration-Free Multi-view Crowd Counting. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13669. Springer, Cham. https://doi.org/10.1007/978-3-031-20077-9_14

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