research-article

Measuring Collectiveness via Refined Topological Similarity

Authors:

Qi WangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 12, Issue 2

Article No.: 34, Pages 1 - 22

https://doi.org/10.1145/2854000

Published: 03 March 2016 Publication History

Abstract

Crowd system has motivated a surge of interests in many areas of multimedia, as it contains plenty of information about crowd scenes. In crowd systems, individuals tend to exhibit collective behaviors, and the motion of all those individuals is called collective motion. As a comprehensive descriptor of collective motion, collectiveness has been proposed to reflect the degree of individuals moving as an entirety. Nevertheless, existing works mostly have limitations to correctly find the individuals of a crowd system and precisely capture the various relationships between individuals, both of which are essential to measure collectiveness. In this article, we propose a collectiveness-measuring method that is capable of quantifying collectiveness accurately. Our main contributions are threefold: (1) we compute relatively accurate collectiveness by making the tracked feature points represent the individuals more precisely with a point selection strategy; (2) we jointly investigate the spatial-temporal information of individuals and utilize it to characterize the topological relationship between individuals by manifold learning; (3) we propose a stability descriptor to deal with the irregular individuals, which influence the calculation of collectiveness. Intensive experiments on the simulated and real world datasets demonstrate that the proposed method is able to compute relatively accurate collectiveness and keep high consistency with human perception.

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Index Terms

Measuring Collectiveness via Refined Topological Similarity
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 12, Issue 2

March 2016

224 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/2837041

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2016 ACM.

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Publication History

Published: 03 March 2016

Accepted: 01 October 2015

Revised: 01 September 2015

Received: 01 April 2015

Published in TOMM Volume 12, Issue 2

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Fundamental Research Funds for the Central Universities
Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences
National Natural Science Foundation of China
Natural Science Foundation Research Project of Shaanxi Province

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Cited By

Wu DXiao EZhu YJensen CLu K(2023) Efficient Retrieval of the Top- k Most Relevant Event-Partner Pairs IEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.311855235:3(2529-2543)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TKDE.2021.3118552
Mohammed MAl-Dhamari ASaeed WAl-Aswadi FSaleh SMarsono M(2023)Motion Pattern-Based Scene Classification Using Adaptive Synthetic Oversampling and Fully Connected Deep Neural NetworkIEEE Access10.1109/ACCESS.2023.332746311(119659-119675)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3327463
Kumari PBedi ASaini M(2023)Multimedia datasets for anomaly detection: a reviewMultimedia Tools and Applications10.1007/s11042-023-17425-z83:19(56785-56835)Online publication date: 13-Dec-2023
https://doi.org/10.1007/s11042-023-17425-z
Pai AChandrahasan PRaghavendra UKarunakar A(2022)Motion pattern-based crowd scene classification using histogram of angular deviations of trajectoriesThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02356-339:2(557-567)Online publication date: 10-Jan-2022
https://dl.acm.org/doi/10.1007/s00371-021-02356-3
Li XChen MWang Q(2020)Quantifying and Detecting Collective Motion in Crowd ScenesIEEE Transactions on Image Processing10.1109/TIP.2020.298528429(5571-5583)Online publication date: 2020
https://doi.org/10.1109/TIP.2020.2985284
Roy ABiswas NSaha SChanda B(2019)Classification of Moving Crowd Based on Motion Pattern2019 IEEE Region 10 Symposium (TENSYMP)10.1109/TENSYMP46218.2019.8971252(102-107)Online publication date: Jun-2019
https://doi.org/10.1109/TENSYMP46218.2019.8971252
Pouyanfar STao YTian HChen SShyu M(2019)Multimodal deep learning based on multiple correspondence analysis for disaster managementWorld Wide Web10.1007/s11280-018-0636-422:5(1893-1911)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s11280-018-0636-4
Zou YZhao XLiu Y(2018)Measuring Crowd Collectiveness by Macroscopic and Microscopic Motion ConsistenciesIEEE Transactions on Multimedia10.1109/TMM.2018.283260120:12(3311-3323)Online publication date: Dec-2018
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Zhang YChang FWang MZhang FHan C(2018)Auxiliary learning for crowd counting via count-netNeurocomputing10.1016/j.neucom.2017.08.018273:C(190-198)Online publication date: 17-Jan-2018
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