Abstract
In this paper we propose a system that is able to distinguish moving and stopped objects in digital image sequences taken from stationary cameras. Our approach is based on self organization through artificial neural networks to construct a model of the scene background and a model of the scene foreground that can handle scenes containing moving backgrounds or gradual illumination variations, helping in distinguishing between moving and stopped foreground regions, leading to an initial segmentation of scene objects. Experimental results are presented for video sequences that represent typical situations critical for detecting vehicles stopped in no parking areas and compared with those obtained by other existing approaches.
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Maddalena, L., Petrosino, A. (2009). 3D Neural Model-Based Stopped Object Detection. In: Foggia, P., Sansone, C., Vento, M. (eds) Image Analysis and Processing – ICIAP 2009. ICIAP 2009. Lecture Notes in Computer Science, vol 5716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04146-4_63
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DOI: https://doi.org/10.1007/978-3-642-04146-4_63
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