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Moving objects detection in thermal scene videos using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm

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Abstract

In this paper, a new algorithm for moving object detection is proposed by using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm. It consists of the following steps: the first contains of classify and estimate the motion vectors between successive frames using the Star diamond search algorithm based on unsupervised Bayesian classifier with Gaussian Expectation of Maximization algorithm, this step serves also to detect the static and dynamic blocks. In the second step, the dynamic blocks are compensated with the white pixels value and the stationary are compensated by black pixels value. In the third step, the morphological opening and closing filters are used for refining the object detected. The proposed approach is trained and evaluated using available infrared (FLIR_ADAS_v2) dataset. The results demonstrate the effectiveness of the proposed method.

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  1. National Polytechnic School of Oran Maurice Audin (Ex- Enset), PB1523, 31000, El M’Naouar, Algeria

    Djoudi Kerfa

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Kerfa, D. Moving objects detection in thermal scene videos using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm. Multimed Tools Appl 83, 6335–6350 (2024). https://doi.org/10.1007/s11042-023-15849-1

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