Calibration-Free Multi-view Crowd Counting
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Computer Vision – ECCV 2022 |
Subtitle of host publication | 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part IX |
Editors | Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner |
Publisher | Springer |
Pages | 227-244 |
ISBN (electronic) | 978-3-031-20077-9 |
ISBN (print) | 9783031200762 |
Publication status | Published - 2022 |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 13669 |
ISSN (Print) | 0302-9743 |
ISSN (electronic) | 1611-3349 |
Conference
Title | 17th European Conference on Computer Vision (ECCV 2022) |
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Location | Hybrid |
Place | Israel |
City | Tel-Aviv |
Period | 23 - 27 October 2022 |
Link(s)
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.
Citation Format(s)
Calibration-Free Multi-view Crowd Counting. / Zhang, Qi; Chan, Antoni B.
Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part IX. ed. / Shai Avidan; Gabriel Brostow; Moustapha Cissé; Giovanni Maria Farinella; Tal Hassner. Springer, 2022. p. 227-244 (Lecture Notes in Computer Science; Vol. 13669).
Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part IX. ed. / Shai Avidan; Gabriel Brostow; Moustapha Cissé; Giovanni Maria Farinella; Tal Hassner. Springer, 2022. p. 227-244 (Lecture Notes in Computer Science; Vol. 13669).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review