IMPLEMENTASI METODE IMPROVED ADAPTIVE GAUSSIAN MIXTURE MODEL BACKGROUND SUBTRACTION DAN HAAR-LIKE FEATURES UNTUK MENGANALISIS STATUS KEPADATAN KENDARAAN YANG MELINTAS DI SUATU JALUR PADA LAMPU LALU LINTAS

2015 ◽  
Author(s):  
Ade Romadhony ◽  
Hamdy Nur Saidy ◽  
Mahmud Dwi Sulistiyo
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Gaussian Mixture ◽  
Model Background

scholarly journals Towards the Development of Effective Video Segmentation Based on Skew Gaussian Mixture Model

2020 ◽  
Vol 9 (3) ◽  
pp. 1735-1745
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Video Segmentation ◽  
Gaussian Mixture ◽  
Dynamic Motion ◽  
Precise Analysis ◽  
Real Time Applications ◽  
Assessment Measures ◽  
Motion Images

video analysis has gained a exponential demand with its usage in security cameras and in most of the real time applications for monitoring the law order. In order to have a precise analysis background subtraction and foreground detection processed are generally considered in the most of the approaches. However ,to have a more precise output from the dynamic motion images, this article proposes a methodology based on skew Gaussian mixture model. The results are analyzed against the existing methods using quality assessment measures.

scholarly journals BMOG: boosted Gaussian Mixture Model with controlled complexity for background subtraction

2018 ◽  
Vol 21 (3) ◽  
pp. 641-654 ◽  
Author(s):  
Isabel Martins ◽  
Pedro Carvalho ◽  
Luís Corte-Real ◽  
José Luis Alba-Castro
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Gaussian Mixture

Background Subtraction Based on Gaussian Mixture Model

2013 ◽  
Vol 694-697 ◽  
pp. 2021-2026
Author(s):  
De Fang Liu ◽  
Ming Deng ◽  
Dai Mu Wang
Keyword(s):  
Probability Density ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Adaptive Algorithm ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Video Sequences ◽  
Recursive Equations ◽  
Gauss Mixture

According to the detection of moving objects in video sequences, the paper puts forward background subtraction based on Gauss mixture model. It analyzes the usual pixel-level approach, and to develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.

Moving Object Detection and Tracking in outside Environments

2011 ◽  
Vol 130-134 ◽  
pp. 3862-3865
Author(s):  
Yi Ding Wang ◽  
Da Qian Li
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Middle Ground ◽  
Moving Objects Detection ◽  
Detection And Tracking ◽  
Objects Detection ◽  
Typical Method

Background subtraction is a typical method for moving objects detection. The Gaussian mixture model is one of widely used method to model the background. However, in challenge environments, quick lighting changes, noises and shake of background can influence the detection of moving objects significantly. To solve this problem, an improved Gaussian Mixture Model is proposed in this paper. In the proposed algorithm, Objects are divided into three categories, foreground, background and middle-ground. The proposed algorithm is a segmented process. Moving objects including foreground and middle-ground are extracted firstly; then foreground is segmented from middle-ground. In this way almost middle-ground are filtered, so we can obtain a clear foreground objects. Experimental results show that the proposed algorithm can detect moving objects much more precisely, and it is robust to lighting changes and shadows.

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