In many algorithms for background modeling, a distribution over feature values is modeled at each pixel. These models, however, do not account for the dependencies that may exist among nearby pixels. The joint domain-range kernel density estimate (KDE) model by Sheikh and Shah [7], which is not a pixel-wise model, represents the background and foreground processes by combining the three color d...

For better background modeling in scenes with nonstationary background, a background modeling algorithm based on adaptive parameter adjustment of the Mixture Gaussian is proposed. Mixture Gaussians is applied to learn the distribution of per-pixel in the temporal domain and to control adaptive adjustment of number K of Gaussian components through in increasing, deleting or merging similar Gauss...

2 Related Works Gaussian mixtures are often used for data modeling in many real-time applications such as video background modeling and speaker direction tracking. The real-time and dynamic nature of these systems prevents the use of a batch EM algorithm. Currently, online learning of mixture models on dynamic data is achieved using an adaptive filter coupled with reassignment rules. However, c...

Many high-level video processing applications such as visual surveillance require the detection and tracking of objects of interest in the video. However, due to inherent changes such as waving trees, water surfaces, ickering lights, etc., the background may not be completely static even with a xed camera. Therefore, background modeling becomes an essential and important part of such applicatio...

In this paper, we proposed a method for reconstructing the 3D model based on continuous sensory input. The robot can draw on extremely large data from the real world using various sensors. However, the sensory inputs are usually too noisy and high-dimensional data. It is very difficult and time consuming for robot to process using such raw data when the robot tries to construct 3D model. Hence,...