Deep learning approaches for crowd counting in images
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Jiang, Minyang
issued: 2021-04-22T16:10:52Z
issued: 2021
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Crowd analysis has been widely used in everyday life. Among different crowd analysis tasks, crowd counting is the most basic but essential one that measures the number of people in a particular area. Such counting and crowd density information is crucial to determine the maximum occupancy of a room or public area to address safety concerns. Counting by hand can yield an accurate number, but it is a tedious job and may not fulfill the time requirement for analysis. Therefore, automated crowd counting for accurately estimating the number of people in crowded scenes is needed. Deep learning has shown beyond human-level accuracy in many computer vision tasks. Recently, researchers have shown that deep learning methods can achieve the state-of-the-art performance in the task of crowd counting in images. The majority of these methods are based on density regression, where a density map is predicted and its integral is further calculated to obtain the final count. However, this learned density map is uninterpretable and could deviate largely from the actual person distribution even if the final count is accurate. Besides, existing crowd counting methods are mainly based on cumbersome feature extraction networks and can not be deployed in edge devices with limited computational power. In this thesis work, we proposed two models that tackle the above interpretability and computational efficiency concerns respectively: the joint crowd counting and localization model extends traditional counting only methods and provides precise localization results without additional model complexity; the ShuffleCount is a computationally efficient and accurate model that is trained through the specially designed knowledge distillation process and can satisfy the real-time crowd analysis requirement on edge devices. We evaluated both models by training and testing on public crowd counting benchmark datasets. Both quantitative and qualitative results are obtained and compared with existing sta…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/77900
note: Deep Learning Approaches for Crowd Counting in ImagesbyMinyang JiangB. Software Engineering, University of Waterloo, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Electrical and Computer Engineering)The University of British Columbia(Vancouver)April 2021© Minyang Jiang, 2021The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Deep Learning Approaches for Crowd Counting in Imagessubmitted by Minyang Jiang in partial fulfillment of the requirements for the de-gree of MASTER OF APPLIED SCIENCE in Electrical and Computer Engi-neering.Examining Committee:Z. Jane Wang, Electrical and Computer Engineering, UBCSupervisorRabab Ward, Electrical and Computer Engineering, UBCCommittee MemberPurang Abolmaesumi, Electrical and Computer Engineering, UBCChairiiAbstractCrowd analysis has been widely used in everyday life. Among different crowdanalysis tasks, crowd counting is the most basic but essential one that measuresthe number of people in a particular area. Such counting and crowd density infor-mation is crucial to determine the maximum occupancy of a room or public areato address safety concerns. Counting by hand can yield an accurate number, butit is a tedious job and may not fulfill the time requirement for analysis. There-fore, automated crowd counting for accurately estimating the number of people incrowded scenes is needed. Deep learning has shown beyond human-level accu-racy in many computer vision tasks. Recently, researchers have shown that deeplearning methods can achieve the state-of-the-art performance in the task of crowdcounting in images. The majority of these methods are based on density regression,where a density map is predicted and its integral is further calculated to obtain thefinal count. However, this learned density map is uninterpretable a…
has type: Thesis/Dissertation
date issued: 2021-05
is shown at: 10.14288/1.0396905
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/77900