Efficient Moving Vehicle Detection Algorithm for Various Traffic Conditions

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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Detection and Classification of Vehicles on Curved Roads

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j1028.08810s19 ◽

2019 ◽

Vol 8 (10S) ◽

pp. 152-156

Keyword(s):

Object Detection ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Traffic Monitoring ◽

Moving Object Detection ◽

Morphological Operations ◽

Moving Vehicles ◽

Blob Analysis

Detection of a vehicle is a very important aspect for traffic monitoring. It is based on the concept of moving object detection. Classifying the detected object as vehicle and class of vehicle is also having application in various application domains. This paper aims at providing an application of vehicle detection and classification concept to detect vehicles along curved roads in Indian scenarios. The main purpose is to ensure safety in such roads. Gaussian mixture model and blob analysis are the methods applied for the detection of vehicles. Morphological operations are used to eliminate noise. The moving vehicles are detected and the class of the vehicle is identified.

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Real-Time Monitoring of Mine Landslide based on Gaussian Mixture Model

Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science - AICS 2019 ◽

10.1145/3349341.3349452 ◽

2019 ◽

Author(s):

Dong Xiao ◽

Qifei He ◽

Hongzong Li ◽

Chenyi Liu

Keyword(s):

Real Time ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Real Time Monitoring

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.814 ◽

2014 ◽

Vol 599-601 ◽

pp. 814-818 ◽

Cited By ~ 1

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.

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Algorithm of Moving Vehicle Detection by Improved Gaussian Mixture Model Based on ROI

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.734.463 ◽

2015 ◽

Vol 734 ◽

pp. 463-467 ◽

Cited By ~ 1

Author(s):

Pan Pan Zhang ◽

Chun Yang Mu ◽

Xing Ma ◽

Fu Lu Xu

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Region Of Interest ◽

Gaussian Mixture ◽

Background Model ◽

Morphological Filter ◽

Improved Method ◽

Moving Vehicle ◽

Model Based ◽

Moving Vehicle Detection

Detection of moving object is a hot topic in computer vision. Traditionally, it is detected for every pixel in whole image by Gaussian mixture background model, which may waste more time and space. In order to improving the computational efficiency, an advanced Gaussian mixture model based on Region of Interest was proposed. Firstly, the solution finds out the most probably region where the target may turn up. And then Gaussian mixture background model is built in this area. Finally, morphological filter algorithm is used for improving integrity of the detected targets. Results show that the improved method could have a more perfect detection but no more time increasing than typical method.

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Detecting Clusters in Atom Probe Data with Gaussian Mixture Models

Microscopy and Microanalysis ◽

10.1017/s1431927617000320 ◽

2017 ◽

Vol 23 (2) ◽

pp. 269-278 ◽

Cited By ~ 13

Author(s):

Jennifer Zelenty ◽

Andrew Dahl ◽

Jonathan Hyde ◽

George D. W. Smith ◽

Michael P. Moody

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Expectation Maximization ◽

Clustering Algorithm ◽

Simulated Data ◽

Atom Probe ◽

Gaussian Mixture ◽

Separation Method ◽

Detection Accuracy ◽

Maximum Separation

AbstractAccurately identifying and extracting clusters from atom probe tomography (APT) reconstructions is extremely challenging, yet critical to many applications. Currently, the most prevalent approach to detect clusters is the maximum separation method, a heuristic that relies heavily upon parameters manually chosen by the user. In this work, a new clustering algorithm, Gaussian mixture model Expectation Maximization Algorithm (GEMA), was developed. GEMA utilizes a Gaussian mixture model to probabilistically distinguish clusters from random fluctuations in the matrix. This machine learning approach maximizes the data likelihood via expectation maximization: given atomic positions, the algorithm learns the position, size, and width of each cluster. A key advantage of GEMA is that atoms are probabilistically assigned to clusters, thus reflecting scientifically meaningful uncertainty regarding atoms located near precipitate/matrix interfaces. GEMA outperforms the maximum separation method in cluster detection accuracy when applied to several realistically simulated data sets. Lastly, GEMA was successfully applied to real APT data.

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Hysteresis-based selective Gaussian-mixture model for real-time background update

10.1117/12.704717 ◽

2007 ◽

Author(s):

Firas Achkar ◽

Aishy Amer

Keyword(s):

Real Time ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Background Update

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Moving Object Detection Based on the Fish

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.1253 ◽

2014 ◽

Vol 644-650 ◽

pp. 1253-1256 ◽

Cited By ~ 1

Author(s):

Lian Li ◽

Jun Yi Song ◽

Zhi Yang Yan

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Industrial Process ◽

Gaussian Mixture ◽

Traffic Monitoring ◽

Detection Algorithm ◽

Moving Object ◽

Moving Target ◽

Target Fish ◽

The Military

The detection and tracking of moving object is the important research of image analysis and understanding as well as in computer vision field, and have extensive application in the traffic monitoring, the military, industrial process control and medical research, but less application in the underwater monitoring of fish. In this paper, in order to be able to real-time detection of the fish in the digital video system moving target, proposed the fish moving target detection algorithm under a camera. With an improved background updating method of adaptive Gaussian mixture model, a method to detect the target fish based on Gaussian mixture model combined with edge detection operator.

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