Real-Time Stopped Object Detection by Neural Dual Background Modeling

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… (More)
DOI: 10.1007/978-3-642-21878-1_44

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Cite this paper

@inproceedings{Gemignani2010RealTimeSO, title={Real-Time Stopped Object Detection by Neural Dual Background Modeling}, author={Giorgio Gemignani and Lucia Maddalena and Alfredo Petrosino}, booktitle={Euro-Par Workshops}, year={2010} }