Background Subtraction In Video Sequences Research Articles

Обгрунтування показника вибору оптимального алгоритму виділення фону у відеопослідовностях з камер відеоспостереження відомчих систем відео спостереження

The background subtraction is an important stage in the process of detecting moving objects in video sequences derived from stationary cameras of video surveillance systems (VSS cameras). variety of approaches to addressing the background selection problem in video sequences from stationary cameras of video surveillance systems has created the need for research on the choice of optimal algorithms. In this paper, we described the problem factors that complicate the background allocation process and described the basic background subtraction algorithms classifications. After analyzing the location of VSS cameras for certain objects within the Information and Telecommunication Systems of the State Border Guard Service and their inspection sectors, we have identified the features of the use of cameras from these systems. We researched the most common algorithms of background subtraction in video sequences, methods of comparative analysis and methodes for selecting optimal background subtraction algorithms in video sequences from stationary VSS cameras. We developed an improved efficiency index for the choice optimal algorithms for the background subtraction in video sequences derived from stationary cameras of video surveillance systems оn the basis method proposed Sobral Andrews and Vacavant Antoine in the Comprehensive review of subtraction algorithms evaluated using synthetic and real video. The essence of the improved method is that we propose to calculate the overall performance of the background subtraction algorithm using Matthews correlation coefficient, because this coefficient takes into account all possible variants of the matrix of algorithm responses (TP, TN, FP, FN). The proposed method was tested by calculating the results of the experimental study in the A comprehensive review of the subtraction algorithms evaluated using synthetic and real videos. As a result, we have developed the index of efficiency of an algorithm of background subtraction , that differs from that offered gathered Sobral Andrews and Vacavant Antoine (FSD), because it takes into account all options matrix classifier responses, and therefore is more accurate than the FSD.

A Fast Convergent Adaptive-K Mixture-Of-Gaussian Model for Video Object Segmentation

It is a key step to extract moving objects from background for computer vision applications. GMM based methods are the most commonly used technique for background subtraction in video sequence. However, how to establish efficient and precision background model with fast convergence rate is a Research-Worthy problem. In this paper, an effective scheme is proposed to accelerate the convergence rate of Adaptive-K Gaussian Mixture Model (AKGMM). The AKGMM algorithm alters the dimension of the parameter space at each pixel based on the changing frequency of pixel value. The number of GMM reflects the complexity of pattern at the pixel. An improved learning method is proposed for Gaussian Mixture Model. An adaptive learning rate is calculated for each Gaussian at every frame for speeding up the convergence without compromising model stability. Experimental results demonstrated that the proposed method gets a faster convergence while maintaining good robustness against complex environment compared to a conventional method.