Automatic foreground detection based on KDE and binary classification

Abstract

In the recent decades, several methods have been developed to extract moving objects in the presence of dynamic background. However, most of them use a global threshold, and ignore the correlation between neighboring pixels. To address these issues, this paper presents a new approach to generate a probability image based on Kernel Density Estimation (KDE) method, and then apply the Maximum A Posteriori in the Markov Random Field (MAP-MRF) based on probability image, so as to generate an energy function, this function will be minimized by the binary graph cut algorithm to detect the moving pixels instead of applying a thresholding step. The proposed method was tested on various video sequences, and the obtained results showed its effectiveness in presence of a dynamic scene, compared to other background subtraction models.

An edge detection mechanism using L*A*B color-based contrast enhancement for underwater images

“…are having several issues in the detection of edges for underwater image [23]. We can utilize the edge detection results in several automatic foreground extraction applications [24,25], but still, it is a challenging issue to detect the efficient edges for applications such as ocean and oceanography. Low Error Rate, Good Localization, minimal response are the three important criteria of the optimal canny edge detection algorithm.…”

Real-time performance evaluation of BGSLibrary algorithms for intelligent surveillance

“…In order to teste the ability and efficiency of the methods in extracting the moving objects under dynamic background, the aforementioned methods are analyzed and compared using the K-way ANONVA. Lahraichi et al [14] presented a new approach through two criteria : the first is based on creating a probabilistic image based on Kernel density estimation method and the second is the extraction of the moving pixels using graph cutting algorithm. Zaharin et al [15] studied different methods of the background subtraction and the frame difference.…”