Using hidden markov model and dempster-shafer theory for evaluating and detecting dangerous situations in level crossing environments

Authors:
Houssam Salmane;Yassine Ruichek;Louahdi Khoudour
Affiliations:
IRTES-SeT, University of Technology Belfort-Montbeliard, Belfort Cedex, France;IRTES-SeT, University of Technology Belfort-Montbeliard, Belfort Cedex, France;LEOST-IFSTTAR, French Institute of Sciences and Technology for Transport, Development and Networks, Villeneuve d'Ascq cedex, France
Venue:
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Year:
2012

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Abstract

In this paper we present a video surveillance system for evaluating and detecting dangerous situations in level crossing environments. The system is composed of the following main parts: a robust algorithm able to detect and separate moving objects in the perceived environment, a Gaussian propagation model based dense optical flow for objects tracking, a Hidden Markov Model to recognize trajectories of detected objects, and an uncertainty model using theory of evidence to calculate the level of danger allowing to detect dangerous situations in level crossings. This method is tested on real image sequences, and the results are discussed. This work is developed within the framework of PANsafer project, supported by the ANR VTT program.