Evidential event inference in transport video surveillance

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Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain

MariaJose.Santofimia@uclm.es

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  • CONFERENCE — Computer Vision and Image Understanding

  • PAGES — 276-297

  • ISSN — 1077-3142

  • VOLUME 144

  • DOI 10.1016/j.cviu.2015.10.017

  • PUBLISHER  Elsevier

  • YEAR  2016

Hong, X., Huang, Y., Ma, W., Varadarajan, S., Miller, P., Liu, W., Santofimia, M.J., … & Zhou, H. (2016). Evidential event inference in transport video surveillance. Computer Vision and Image Understanding144, 276-297.

@article{hong2016evidential,
title={Evidential event inference in transport video surveillance},
author={Hong, Xin and Huang, Yan and Ma, Wenjun and Varadarajan, Sriram and Miller, Paul and Liu, Weiru and Romero, Maria Jose Santofimia and del Rincon, Jesus Martinez and Zhou, Huiyu},
journal={Computer Vision and Image Understanding},
volume={144},
pages={276--297},
year={2016},
publisher={Elsevier}
}

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.

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