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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/21804
Title: Crowd Characterization in Surveillance Videos Using Deep-Graph Convolutional Neural Network
Authors: Behera S.
Dogra D.P.
Bandyopadhyay M.K.
Pratim Roy, Partha
Published in: IEEE Transactions on Cybernetics
Abstract: Crowd behavior is a natural phenomenon that can provide valuable insight into the crowd characterization process. Modeling the visual appearance of a large crowd gathering can reveal meaningful information about its dynamics. Parametric modeling can be used to develop efficient and robust crowd monitoring systems. A crowd can be structured or unstructured based on the organization. In this article, crowd characterization has been mapped to a graph classification problem to classify movements based on order parameter (φ), active force components, and steadiness (Reynolds number). The graphs are constructed from the motion groups obtained using an active Langevin framework. These graphs are processed using a deep graph convolutional neural network for crowd characterization. For experimentation, we have prepared a dataset comprising of videos from popular publicly available datasets and our own recorded videos. The proposed framework has been compared with the latest deep learning-based frameworks in terms of accuracy and area under the curve (AUC). We have obtained a 4%-5% improvement in accuracy and AUC values over the existing frameworks. The insights obtained from the proposed framework can be used for better crowd monitoring and management. IEEE
Citation: IEEE Transactions on Cybernetics
URI: https://doi.org/10.1109/TCYB.2021.3126434
http://repository.iitr.ac.in/handle/123456789/21804
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Analytical models
Computational modeling
Convolutional neural networks
Crowd analysis
crowd characterization
crowd organization
deep graph convolutional neural network (DGCNN)
Force
graph classification
Langevin equation
Mathematical models
Microscopy
structured crowd
unstructured crowd
Videos
visual surveillance
ISSN: 21682267
Author Scopus IDs: 57215202701
35408975400
23099055900
56880478500
Author Affiliations: Behera, S., School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar 752050, India (e-mail: sb46@iitbbs.ac.in)
Dogra, D.P., School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar 752050, India.
Bandyopadhyay, M.K., School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar 752050, India.
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India.
Appears in Collections:Journal Publications [CS]

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