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Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection

Belhadi, Asma; Djenouri, Youcef; Srivastava, Gautam; Djenouri, Djamel; Lin, Jerry Chun-Wei; Fortino, Giancarlo

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection Thumbnail


Authors

Asma Belhadi

Youcef Djenouri

Gautam Srivastava

Jerry Chun-Wei Lin

Giancarlo Fortino



Abstract

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories. First, algorithms based on data mining and knowledge discovery, which study the different correlation among human behavioral data, and identify the collective abnormal human behavior from knowledge extracted. Secondly, algorithms exploring convolution deep neural networks, which learn different features of historical data to determine the collective abnormal human behaviors. Experiments on an actual human behaviors database have been carried out to demonstrate the usefulness of the proposed algorithms. The results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance. In particular, for large datasets, the accuracy of the deep learning solution reaches 88%, however other solutions do not exceed 81%. Additionally, the runtime of the deep learning solution is below 50 seconds, whereas other solutions need more than 80 seconds for analyzing the same database.

Citation

Belhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Lin, J. C., & Fortino, G. (2021). Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection. Information Fusion, 65, 13-20. https://doi.org/10.1016/j.inffus.2020.08.003

Journal Article Type Article
Acceptance Date Aug 3, 2020
Online Publication Date Aug 8, 2020
Publication Date Jan 1, 2021
Deposit Date Apr 8, 2021
Publicly Available Date Apr 9, 2021
Journal Information Fusion
Print ISSN 1566-2535
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 65
Pages 13-20
DOI https://doi.org/10.1016/j.inffus.2020.08.003
Keywords Signal Processing; Hardware and Architecture; Software; Information Systems
Public URL https://uwe-repository.worktribe.com/output/7249413
Additional Information This article is maintained by: Elsevier; Article Title: Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection; Journal Title: Information Fusion; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.inffus.2020.08.003; Content Type: article; Copyright: © 2020 The Authors. Published by Elsevier B.V.

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