In this paper, for the modern intelligent video surveillance, we introduce an optimizing motion detection algorithm aim at overcoming the flaw of conventional background subtraction algorithm. We combine adaptive background model in HSV color space with moving object segmentation based on fuzzy clustering to extract moving objects from frame. The adaptive background model is able to restoring t...

Sapienza University of Rome, Italy 1.

Mixture of Gaussians (MOG) has been widely used for robustly modeling complicated backgrounds, especially those with small repetitive movements (such as leaves, bushes, rotating fan, ocean waves, rain). The performance of MOG can be greatly improved by tackling several practical issues. In this paper, we quantitatively evaluate (using the Wallflower benchmarks) the performance of the MOG. with ...

Several signal subspace techniques have been recently suggested for the extraction of the visual evoked potential signals from brain background colored noise. The majority of these techniques assume the background noise as white, and for colored noise, it is suggested to be whitened, without further elaboration on how this might be done. In this paper, we investigate the whitening capabilities ...

In this research, we present a novel dynamic background modeling method based on reconstructed 3D shapes, which can solve background modeling problems of multi-camera in real-time. While 3D shape reconstruction is a popular technology widely used for detecting, tracking or identifying various objects, little effort has been made in applying this useful method to background subtraction. In this ...

Background subtraction methods are widely exploited for moving object detection in videos in many computer vision applications, such as traffic monitoring, human motion capture and video surveillance. The two most distinguishing and challenging aspects of such approaches in this application field are how to build correctly and efficiently the background model and how to prevent the false detect...

Background subtraction is an essential processing component for many video applications. However, its development has largely been application driven and done in ad hoc manners. In this paper, we provide a Bayesian formulation of background segmentation based on Gaussian mixture models. We show that the problem consists of two density estimation problems, one application independent one depende...

Background subtraction is often one of the first tasks involved in video surveillance applications. Classical methods use a statistical background model and compute a distance between each part (pixel or bloc) of the current frame and the model to detect moving targets. Segmentation is then obtained by thresholding this distance. This commonly used approach suffers from two main drawbacks. Firs...

For the realistic simulation of embodied agents we need a model of emotion that represents both structural and dynamic aspects of emotional phenomena to serve as background support for multifaceted emotion characterization. In this paper we present an emotion model oriented towards that aim, which provides a continuous modeling of the evolution of emotional phenomena. We also illustrate how it ...

The Argo-derived background diapycnal mixing (BDM) proposed by Deng et al. (in publish) is introduced to and applied in Hybrid Coordinate Ocean Model (HYCOM). Sensitive experiments are carried out using HYCOM to detect the responses of ocean surface temperature and Meridional Overturning Circulation (MOC) to BDM in a global context. Preliminary results show that utilizing a constant BDM, with t...