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Moving objects are usually detected by measuring the appearance change from a background model. The background model should adapt to slow changes such as illumination, but detect faster changes caused by moving objects. Particle filters do an excellent task in modeling non parametric distributions as needed for a background model, but may adapt too quickly to the foreground objects. A persistent particle...
In this paper, an adaptive skin color model switching based on AdaBoost method for face tracking is proposed. Possible skin clusters under illumination varying scenes are detected by an optimal skin color model, which is adaptively selected by a well-defined quality measure. The possible facial candidates are further validated by AdaBoost to determine whether human faces exist in video sequences or...
In this paper we present a robust method for background subtraction from a fixed camera in video surveillance system. The background subtraction is an important part of object tracking and many algorithms have been proposed for decades. Mixture of Gaussian for those in this paper is very famous and used widely. We present the robust method that can adapt the background model to various situations...
In this paper we present a supervised method of image segmentation based on the statistic approach expressed in an hybrid space constituted by the three relevant chromatic level deduced by histogram analysis approach, this technique may the possibility of adapting the treatments to the local context of image with a little priori knowledge. This method has been applied on colour images issued from...
Adaptive Local Image Registration based on adaptive filtering can register both grayscale images and color images. Here the local distortions are corrected without explicitly estimating the displacement field. A filter of appropriate size convolves with reference image and gives the pixel values corresponding to the distorted image and the filter is updated in each stage of the convolution. When the...
This paper presents a physics-based approach capable of detecting cast shadows in video sequence effectively. We develop a new physical model of cast shadows without making prior assumption of the spectral power distribution (SPD) of the light sources and ambient illumination in the scene. The background appearance variation caused by cast shadows is characterized as the interaction of the blocked...
Aiming at improving the performance of non-rigid object tracking in video sequences acquired by a stationary camera, an effective method based on the adaptive color segmentation and object part model was presented. In this work, we modeled background and obtained the foreground blobs with an effective adaptive background updating method based on Gaussian mixture model (GMM), and then the regions in...
In this paper, a novel algorithm for foreground detection and shadow removal is presented. The proposed method employs a region-based approach by processing two foregrounds resulted from gradient-and color-based background subtraction methods. The performance of the system is compared against conventional approaches for five indoor and outdoor video sequences. Experimental results confirm that the...
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