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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Gemignani, G., Maddalena, L., & Petrosino, A. (2011). Real-time stopped object detection by neural dual background modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6586 LNCS, pp. 357–364). https://doi.org/10.1007/978-3-642-21878-1_44
Mendeley helps you to discover research relevant for your work.