3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels

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

31 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationThe Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20)
Place of PublicationCalifornia
PublisherAAAI Press
Pages12837-12844
Number of pages8
ISBN (print)9781577358350 (10 issue set)
Publication statusPublished - Feb 2020

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number7
Volume34
ISSN (Print)2159-5399
ISSN (electronic)2374-3468

Conference

Title34th AAAI Conference on Artificial Intelligence (AAAI-20)
PlaceUnited States
CityNew York
Period7 - 12 February 2020

Abstract

Crowd counting has been studied for decades and a lot of works have achieved good performance, especially the DNNs-based density map estimation methods. Most existing crowd counting works focus on single-view counting, while few works have studied multi-view counting for large and wide scenes, where multiple cameras are used. Recently, an end-to-end multi-view crowd counting method called multi-view multi-scale (MVMS) has been proposed, which fuses multiple camera views using a CNN to predict a 2D scene-level density map on the ground-plane. Unlike MVMS, we propose to solve the multi-view crowd counting task through 3D feature fusion with 3D scene-level density maps, instead of the 2D ground-plane ones. Compared to 2D fusion, the 3D fusion extracts more information of the people along z-dimension (height), which helps to solve the scale variations across multiple views. The 3D density maps still preserve the 2D density maps property that the sum is the count, while also providing 3D information about the crowd density. We also explore the projection consistency among the 3D prediction and the ground-truth in the 2D views to further enhance the counting performance. The proposed method is tested on 3 multi-view counting datasets and achieves better or comparable counting performance to the state-of-the-art.

Citation Format(s)

3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels. / Zhang, Qi; Chan, Antoni B.
The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). California: AAAI Press, 2020. p. 12837-12844 (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 34, No. 7).

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