Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/41189
Title: Evidential event inference in transport video surveillance
Authors: Hong, Xin
Huang, Yan
Ma, Wenjun
Varadarajan, Sriram
Miller, Paul
Liu, Weiru
Santofimia Romero, Maria Jose
del Rincon, Jesus Martinez
Zhou, Huiyu
First Published: 1-Apr-2016
Publisher: Elsevier for Academic Press
Citation: Computer Vision and Image Understanding, 2016, 144, pp. 276-297 (22)
Abstract: This paper presents a new framework for multi-subject event inference in surveillance video, where measurements produced by low-level vision analytics usually are noisy, incomplete or incorrect. Our goal is to infer the composite events undertaken by each subject from noise observations. To achieve this, we consider the temporal characteristics of event relations and propose a method to correctly associate the detected events with individual subjects. The Dempster–Shafer (DS) theory of belief functions is used to infer events of interest from the results of our vision analytics and to measure conflicts occurring during the event association. Our system is evaluated against a number of videos that present passenger behaviours on a public transport platform namely buses at different levels of complexity. The experimental results demonstrate that by reasoning with spatio-temporal correlations, the proposed method achieves a satisfying performance when associating atomic events and recognising composite events involving multiple subjects in dynamic environments.
DOI Link: 10.1016/j.cviu.2015.10.017
ISSN: 1077-3142
eISSN: 1090-235X
Links: https://www.sciencedirect.com/science/article/pii/S1077314215002477?via%3Dihub
http://hdl.handle.net/2381/41189
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2016, Elsevier for Academic Press. Deposited with reference to the publisher’s open access archiving policy.
Description: Supplementary material associated with this article can be found, in the online version, at 10.1016/j.cviu.2015.10.017. This is open data under the CC BY license http://creativecommons.org/licenses/by/4.0/
Appears in Collections:Published Articles, Dept. of Computer Science

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