Hardware implementation of the Gaussian Mixture Model foreground object segmentation algorithm working with ultra-high resolution video stream in real-time

2018 ◽  
Author(s):  
Piotr Janus ◽  
Tomasz Kryjak
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Hardware Implementation ◽  
Object Segmentation ◽  
Gaussian Mixture ◽  
Video Stream ◽  
Segmentation Algorithm ◽  
Foreground Object

An Improved Algorithm for Real-Time Moving Target Detection Based on Gaussian Mixture Model

2014 ◽  
Vol 599-601 ◽  
pp. 814-818 ◽  
Author(s):  
Xue Yuan Chen ◽  
Xia Fu Lv ◽  
Jie Liu
Keyword(s):  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Detection System ◽  
Gaussian Mixture ◽  
Learning Rate ◽  
Model Parameters ◽  
Illumination Variation ◽  
Matching Algorithm ◽  
Passive Learning

Gaussian Mixture Model is a popular method to detect moving targets for static cameras. Since the traditional Gaussian Mixture Model has a poor adaptability when the illumination is changing in the scene and has passive learning rate, this paper describes a method that can detect illumination variation and update the learning rate adaptively. It proposes an approach which uses the color histogram matching algorithm and adjusts the learning rate automatically after introducing illumination variation factor and model parameters. Furthermore, the proposed method can select the number of describing model component adaptively, so this method reduced the computation complexity and improved the real-time performance. The experiment results indicate that the detection system gets better robustness, adaptability and stability.

Real-time online learning of Gaussian mixture model for opacity mapping

2016 ◽  
Vol 211 ◽  
pp. 212-220 ◽  
Author(s):  
Guo Zhou ◽  
Dengming Zhu ◽  
Yi Wei ◽  
Zhaoqi Wang ◽  
Yongquan Zhou
Keyword(s):  
Online Learning ◽  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture

scholarly journals Efficient Moving Vehicle Detection Algorithm for Various Traffic Conditions

2019 ◽  
Vol 8 (3) ◽  
pp. 6069-6076
Keyword(s):  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Visual Surveillance ◽  
Detection Accuracy ◽  
Data Set ◽  
Moving Vehicle ◽  
Detection Techniques

Many computer vision applications needs to detect moving object from an input video sequences. The main applications of this are traffic monitoring, visual surveillance, people tracking and security etc. Among these, traffic monitoring is one of the most difficult tasks in real time video processing. Many algorithms are introduced to monitor traffic accurately. But most of the cases, the detection accuracy is very less and the detection time is higher which makes the algorithms are not suitable for real time applications. In this paper, a new technique to detect moving vehicle efficiently using Modified Gaussian Mixture Model and Modified Blob Detection techniques is proposed. The modified Gaussian Mixture model generates the background from overall probability of the complete data set and by calculating the required step size from the frame differences. The modified Blob Analysis is then used to classify proper moving objects. The simulation results shows that the method accurately detect the target

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