Abstract
Moving object detection is a relevant step for many computer vision applications, and specifically for real-time color video surveillance systems, where processing time is a challenging issue. We adopt a dual background approach for detecting moving objects and discriminating those that have stopped, based on a neural model capable of learning from past experience and efficiently detecting such objects against scene variations. We propose a GPGPU approach allowing real-time results, by using a mapping of neurons on a 2D flat grid on NVIDIA CUDA. Several experiments show parallel perfomance and how our approach outperforms with respect to OpenMP implementation.
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Gemignani, G., Maddalena, L., Petrosino, A. (2011). Real-Time Stopped Object Detection by Neural Dual Background Modeling. In: Guarracino, M.R., et al. Euro-Par 2010 Parallel Processing Workshops. Euro-Par 2010. Lecture Notes in Computer Science, vol 6586. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21878-1_44
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DOI: https://doi.org/10.1007/978-3-642-21878-1_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21877-4
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