High quality online video segmentation is a very challenging task. Among various cues to infer the segmentation, the foreground and background color distributions are the most important. However, previous color modeling methods are error-prone when some parts of the foreground and background have similar colors, to address this problem, we propose a novel approach of Confidence-based Color Mode...

We describe our approach to segmenting moving objects from the color video data supplied by a nominally stationary camera. There are two main contributions in our work. The first contribution augments Zivkovic and Heijden’s recursively updated Gaussian mixture model approach, with a multidimensional Gaussian kernel spatio-temporal smoothing transform. We show that this improves the segmentation...

We present a novel method to robustly and efficiently detect moving object, even under the complexity background, such as illumination changes, long shadows etc. This work is distinguished by three key contributions. The first is the integration of the Local Binary Pattern texture measure which extends the moving object detection work for light illumination changing. The second is the introduct...

This paper describes an efficient background subtraction technique for detecting moving objects. The proposed approach is able to overcome difficulties like illumination changes and moving shadows. Our method introduces two discriminative features based on angular and modular patterns, which are formed by similarity measurement between two sets of RGB color vectors: one belonging to the backgro...

This paper presents an innovative method to interpret the content of a video scene using a depth camera. Cameras that provide distance instead of color information are part of a promising young technology but they come with many di culties: noisy signals, small resolution, and ambiguities, to cite a few. By taking advantage of the robustness to noise of a recent background subtraction algorithm...

Introducing suitable features in the scribble-based foreground-background (Fg/Bg) segmentation problem is crucial. In many cases, the object of interest has different regions with different color modalities. The same applies to a non-uniform background. Fg/Bg color modalities can even overlap when the appearance is solely modeled using color spaces like RGB or Lab. In this paper, we purposefull...

In the new MPEG-4 video coding standard, aut omatic video object segmentation plays a key role in supporting object-oriented coding and enabling content -based functionalities. Background subtraction is one of the basic automatic video object segmentation methods. But various environmental illumination conditions often make it hard to work. A robust background subtraction method is presented in...

In SU(2) gluodynamics we calculate the gluon polarization tensor in an Abelian homogeneous magnetic field in one-loop order in the Lorentz background field gauge. It turned out to be non transversal and consisting of ten tensor structures and corresponding form factors four in color neutral and six in color charged sector. Seven tensor structures are transversal, three are not. The non transver...

In addition to being invariant to image rotation and translation, histograms have the advantage of being easy to compute. These advantages make histograms very popular in computer vision. However, without data quantization to reduce size, histograms are generally not suitable for realtime applications. Moreover, they are sensitive to quantization errors and lack any spatial information. This pa...

Background subtraction is one of the most commonly used components in machine vision systems. Despite the numerous algorithms proposed in the literature and used in practical applications, key challenges remain in designing a single system that can handle diverse environmental conditions. In this paper we present Multiple Background Model based Background Subtraction Algorithm as such a candida...