Abstract
Most of object detection algorithms do not yield perfect foreground segmentation masks. These errors in the initial stage of video surveillance systems could cause that the subsequent tasks like object tracking and behavior analysis, can be extremely compromised. In this paper, we propose a methodology based on self-organizing neural networks and histogram analysis, which detects unusual objects in the scene and improve the foreground mask handling occlusions between objects. Experimental results on several traffic sequences found in the literature show that the proposed methodology is promising and suitable to correct segmentation errors on crowded scenes with rigid objects.
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Luque-Baena, R.M., López-Rubio, E., Domínguez, E., Palomo, E.J., Jerez, J.M. (2013). A Self-organizing Map for Traffic Flow Monitoring. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_49
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DOI: https://doi.org/10.1007/978-3-642-38682-4_49
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