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.
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Salmane, H., Ruichek, Y., Khoudour, L. (2013). Using Hidden Markov Model and Dempster-Shafer Theory for Evaluating and Detecting Dangerous Situations in Level Crossing Environments. In: Batyrshin, I., González Mendoza, M. (eds) Advances in Artificial Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37807-2_12
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DOI: https://doi.org/10.1007/978-3-642-37807-2_12
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