EGMM video surveillance for monitoring urban traffic scenario

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
A. Reyana ◽  
Sandeep Kautish ◽  
A.S. Vibith ◽  
S.B. Goyal
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Urban Traffic ◽  
Content Type ◽  
Proposed Model ◽  
Traffic Scenario ◽  
Slow Moving ◽  
Contamination Problem

PurposeIn the traffic monitoring system, the detection of stirring vehicles is monitored by fitting static cameras in the traffic scenarios. Background subtraction a commonly used method detaches poignant objects in the foreground from the background. The method applies a Gaussian Mixture Model, which can effortlessly be contaminated through slow-moving or momentarily stopped vehicles.Design/methodology/approachThis paper proposes the Enhanced Gaussian Mixture Model to overcome the addressed issue, efficiently detecting vehicles in complex traffic scenarios.FindingsThe model was evaluated with experiments conducted using real-world on-road travel videos. The evidence intimates that the proposed model excels with other approaches showing the accuracy of 0.9759 when compared with the existing Gaussian mixture model (GMM) model and avoids contamination of slow-moving or momentarily stopped vehicles.Originality/valueThe proposed method effectively combines, tracks and classifies the traffic vehicles, resolving the contamination problem that occurred by slow-moving or momentarily stopped vehicles.

EGMM Video Surveillance for Monitoring Urban Traffic Scenario

2020 ◽  
Vol 13 ◽  
Author(s):  
Reyana. A ◽  
Sandeep Kautish
Keyword(s):  
Monitoring System ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Processing Technique ◽  
Urban Traffic ◽  
Image Processing Technique ◽  
Traffic Scenario ◽  
Slow Moving

Objective: In the traffic monitoring system, the detection of stirring vehicles is monitored by fitting staticcameras in the traffic scenarios. The background subtraction, a commonly used method detaches poignant objects in the foreground from the background. The method applies a Gaussian Mixture Model, which can effortlessly be contaminated through slow-moving or momentarily stopped vehicles. For decades traffic vehicle-monitoring system follows fixedcamera surveillance for recording and extracting useful information. Calculating the number of Gaussian Models pixelwise the processing time of the observed scene can be calculated. However, an effective method to describe the smooth behavior of traffic scenes to handle critical situations is required. This paper proposes the method to effectively combine, track, and classify the traffic vehicles, resolving the contamination problem that occurred by slow-moving or momentarily stopped vehicles. Methods: The present study proposes an Enhanced Gaussian Mixture Model to overcome the addressed issue, efficiently detecting vehicles in complex traffic scenarios. The model was evaluated with experiments conducted using real-world on-road travel videos. Results: Compared with the existing GMM model to show contamination avoidance of vehicles that are motionless for a time gap. Conclusion: The findings present an improvement in the image processing technique for processing effective video scenes to eliminate frictional and noise variations. The Enhanced Gaussian Mixture Model shows a better accuracy of 0.9759 when compared with the existing state-of-the-art model and avoids contamination of slow-moving or momentarily stopped vehicles.

scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

scholarly journals Detection and Classification of Vehicles on Curved Roads

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.

Moving Object Detection Based on the Fish

2014 ◽  
Vol 644-650 ◽  
pp. 1253-1256 ◽  
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.

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

A contour-guided pose alignment method based on Gaussian mixture model for precision assembly

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pengyue Guo ◽  
Zhijing Zhang ◽  
Lingling Shi ◽  
Yujun Liu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Assembly System ◽  
Alignment Method ◽  
Content Type ◽  
Point Set Registration ◽  
Contour Point ◽  
Point Set

Purpose The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system. Design/methodology/approach A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets. Findings The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance. Originality/value Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.

Cloud robotic grasping of Gaussian mixture model based on point cloud projection under occlusion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shifeng Lin ◽  
Ning Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Working Environment ◽  
Vision Sensor ◽  
Content Type ◽  
Cloud Robotics ◽  
Camera View ◽  
Robot Grasping

Purpose In multi-robot cooperation, the cloud can share sensor data, which can help robots better perceive the environment. For cloud robotics, robot grasping is an important ability that must be mastered. Usually, the information source of grasping mainly comes from visual sensors. However, due to the uncertainty of the working environment, the information acquisition of the vision sensor may encounter the situation of being blocked by unknown objects. This paper aims to propose a solution to the problem in robot grasping when the vision sensor information is blocked by sharing the information of multi-vision sensors in the cloud. Design/methodology/approach First, the random sampling consensus algorithm and principal component analysis (PCA) algorithms are used to detect the desktop range. Then, the minimum bounding rectangle of the occlusion area is obtained by the PCA algorithm. The candidate camera view range is obtained by plane segmentation. Then the candidate camera view range is combined with the manipulator workspace to obtain the camera posture and drive the arm to take pictures of the desktop occlusion area. Finally, the Gaussian mixture model (GMM) is used to approximate the shape of the object projection and for every single Gaussian model, the grabbing rectangle is generated and evaluated to get the most suitable one. Findings In this paper, a variety of cloud robotic being blocked are tested. Experimental results show that the proposed algorithm can capture the image of the occluded desktop and grab the objects in the occluded area successfully. Originality/value In the existing work, there are few research studies on using active multi-sensor to solve the occlusion problem. This paper presents a new solution to the occlusion problem. The proposed method can be applied to the multi-cloud robotics working environment through cloud sharing, which helps the robot to perceive the environment better. In addition, this paper proposes a method to obtain the object-grabbing rectangle based on GMM shape approximation of point cloud projection. Experiments show that the proposed methods can work well.

A hybrid model coupling extreme gradient boosting model with Gaussian mixture model for streamflow forecasting

2021 ◽  
Author(s):  
Lingling Ni ◽  
Dong Wang ◽  
Jianfeng Wu ◽  
Yuankun Wang
Keyword(s):  
Water Resources ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Hybrid Model ◽  
Gaussian Mixture ◽  
Gradient Boosting ◽  
Streamflow Forecasting ◽  
Modular Model ◽  
Proposed Model ◽  
Extreme Gradient Boosting

<p>With the increasing water requirements and weather extremes, effective planning and management for water issues has been of great concern over the past decades. Accurate and reliable streamflow forecasting is a critical step for water resources supply and prevention of natural disasters. In this study, we developed a hybrid model (namely GMM-XGBoost), coupling extreme gradient boosting (XGBoost) with Gaussian mixture model (GMM), for monthly streamflow forecasting. The proposed model is based on the principle of modular model, where a complex problem is divided into several simple ones. GMM was applied to cluster streamflow into several groups, using the features selected by a tree-based method. Then, each group was used to fit several single XGBoosts. And the prediction is a weighted average of the single models. Two streamflow datasets were used to evaluate the performance of the proposed model. The prediction accuracy of GMM-XGBoost was compared with that of support vector machine (SVM) and standalone XGBoost. The results indicated that although all three models yielded quite good performance on one-month ahead forecasting with high Nash-Sutclitte efficiency coefficient (NSE) and low root mean squared error (RMSE), GMM-XGBoost provided the best accuracy with significant improvement of forecasting accuracy. It can be inferred from the results that (1) XGBoost is applicable for streamflow forecasting, and in general, performs better than SVM; (2) the cluster analysis-based modular model is helpful in improving accuracy; (3) the proposed GMM-XGBoost model is a superior alternative, which can provide accurate and reliable predictions for optimal water resources management.</p><p>Note: This study has been published in Journal of Hydrology (Ni, L., Wang, D., Wu, J., Wang, Y., Tao, Y., Zhang, J. and Liu, J., 2020. Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model. Journal of Hydrology, 586.).</p>

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