Calibration-Free Multi-view Crowd Counting

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

10 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2022
Subtitle of host publication17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part IX
EditorsShai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
PublisherSpringer
Pages227-244
ISBN (electronic)978-3-031-20077-9
ISBN (print)9783031200762
Publication statusPublished - 2022

Publication series

NameLecture Notes in Computer Science
Volume13669
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Conference

Title17th European Conference on Computer Vision (ECCV 2022)
LocationHybrid
PlaceIsrael
CityTel-Aviv
Period23 - 27 October 2022

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).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review