Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes

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

23 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Subtitle of host publicationProceedings
Pages5353-5362
Publication statusPublished - Jun 2018

Publication series

Name
ISSN (Print)2575-7075

Conference

Title31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)
LocationCalvin L. Rampton Salt Palace Convention Center
PlaceUnited States
CitySalt Lake City
Period18 - 22 June 2018

Abstract

While visual tracking has been greatly improved over the recent years, crowd scenes remain particularly challenging for people tracking due to heavy occlusions, high crowd density, and significant appearance variation. To address these challenges, we first design a Sparse Kernelized Correlation Filter (S-KCF) to suppress target response variations caused by occlusions and illumination changes, and spurious responses due to similar distractor objects. We then propose a people tracking framework that fuses the S-KCF response map with an estimated crowd density map using a convolutional neural network (CNN), yielding a refined response map. To train the fusion CNN, we propose a two-stage strategy to gradually optimize the parameters. The first stage is to train a preliminary model in batch mode with image patches selected around the targets, and the second stage is to fine-tune the preliminary model using the real frame-by-frame tracking process. Our density fusion framework can significantly improves people tracking in crowd scenes, and can also be combined with other trackers to improve the tracking performance. We validate our framework on two crowd video datasets.

Citation Format(s)

Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes. / Ren, Weihong; Kang, Di; Tang, Yandong et al.
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018: Proceedings. 2018. p. 5353-5362 8578659.

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