Adaptive Density Map Generation for Crowd Counting

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

128 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Computer Vision
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages1130-1139
ISBN (electronic)9781728148038
ISBN (print)9781728148045
Publication statusPublished - Oct 2019

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Number2380-7504
Volume2019-October
ISSN (Print)1550-5499

Conference

Title17th IEEE/CVF International Conference on Computer Vision (ICCV 2019)
LocationCOEX Convention Center
PlaceKorea, Republic of
CitySeoul
Period27 October - 2 November 2019

Abstract

Crowd counting is an important topic in computer vision due to its practical usage in surveillance systems. The typical design of crowd counting algorithms is divided into two steps. First, the ground-truth density maps of crowd images are generated from the ground-truth dot maps (density map generation), e.g., by convolving with a Gaussian kernel. Second, deep learning models are designed to predict a density map from an input image (density map estimation). Most research efforts have concentrated on the density map estimation problem, while the problem of density map generation has not been adequately explored. In particular, the density map could be considered as an intermediate representation used to train a crowd counting network. In the sense of end-to-end training, the hand-crafted methods used for generating the density maps may not be optimal for the particular network or dataset used. To address this issue, we first show the impact of different density maps and that better ground-truth density maps can be obtained by refining the existing ones using a learned refinement network, which is jointly trained with the counter. Then, we propose an adaptive density map generator, which takes the annotation dot map as input, and learns a density map representation for a counter. The counter and generator are trained jointly within an end-to-end framework. The experiment results on popular counting datasets confirm the effectiveness of the proposed learnable density map representations.

Citation Format(s)

Adaptive Density Map Generation for Crowd Counting. / Wan, Jia; Chan, Antoni.
Proceedings - 2019 International Conference on Computer Vision. Institute of Electrical and Electronics Engineers, Inc., 2019. p. 1130-1139 9009065 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2019-October, No. 2380-7504).

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