scholarly journals Research on Design of Intelligent Background Differential Model for Training Target Monitoring

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ya Liu ◽  
Fusheng Jiang ◽  
Yuhui Wang ◽  
Lu OuYang ◽  
Bo Gao ◽  
...  
Keyword(s):  
Difference Method ◽  
Detection Efficiency ◽  
Moving Targets ◽  
Subsequent Work ◽  
Differential Model ◽  
Colour Image ◽  
Huge Impact ◽  
The Difference ◽  
Target Monitoring ◽  
Background Difference

The detection of moving targets is to detect the change area in a sequence of images and extract the moving targets from the background image. It is the basis. Whether the moving targets can be correctly detected and segmented has a huge impact on the subsequent work. Aiming at the problem of high failure rate in the detection of sports targets under complex backgrounds, this paper proposes a research on the design of an intelligent background differential model for training target monitoring. This paper proposes a background difference method based on RGB colour separation. The colour image is separated into independent RGB three-channel images, and the corresponding channels are subjected to the background difference operation to obtain the foreground image of each channel. In order to retain the difference of each channel, the information of the foreground images of the three channels is fused to obtain a complete foreground image. The feature of the edge detection is not affected by light; the foreground image is corrected. From the experimental results, the ordinary background difference method uses grey value processing, and some parts of the target with different colours but similar grey levels to the background cannot be extracted. However, the method in this paper can better solve the defect of misdetection. At the same time, compared with traditional methods, it also has a higher detection efficiency.

Study on Moving Object Detection Based on RGB Color Model

2013 ◽  
Vol 710 ◽  
pp. 700-703
Author(s):  
Chun Yang Liu ◽  
Dao Zheng Hou ◽  
Chang An Liu
Keyword(s):  
Difference Method ◽  
Moving Objects ◽  
Color Image ◽  
Three Dimensional ◽  
Color Model ◽  
Current Frame ◽  
Gray Image ◽  
Average Value ◽  
The Difference ◽  
Background Difference

The traditional background difference method is based on gray image. Some information is lost when color image is transformed into gray image. So it is difficult to discriminate different colors with similar gray values and easily disturbed by noise and shadows. In this paper, the background difference is based on RGB color model. It is proposed to use the average value of each pixel of the color image sequences to extract the background, and then use the three-dimensional color values of the current frame and background image to compute the difference to detect the moving objects. The proposed approach is simple and easy to implement. The experimental results show that it is more sensitive to colors and has higher accuracy and robustness than the traditional background difference method. Besides, it is more resistant to shadows.

scholarly journals Video Target Detection In Underground Mine Based On Background Difference And Edge Detection

2018 ◽  
Vol 232 ◽  
pp. 02023
Author(s):  
Shangfu Gong ◽  
Fengzhi Xu ◽  
Pengtao Jia
Keyword(s):  
Edge Detection ◽  
Background Subtraction ◽  
Detection Efficiency ◽  
Dynamic Environment ◽  
Underground Mine ◽  
Surveillance Video ◽  
Moving Targets ◽  
Canny Edge ◽  
Background Difference ◽  
Improved Algorithm

In view of the complex environment in the underground mine, the detection of moving targets in surveillance video often had the problems of low detection efficiency and the detection result was greatly affected by noise and shadows. A target extraction method based on fusion background subtraction, inter-frame difference and edge detection was proposed. Firstly, the method used the hybrid gaussian background modeling (GMM) to obtain the accurate background image of the dynamic environment, and the extracted moving targets by using background subtractiont. Then based on the three-frame differential and Canny edge detection, the foreground image and the moving object blob was obtained, which was combined with the background subtraction to eliminate noise and voids, and to avoid missed detection of the moving target. Finally, the shadows in the detection process were removed through pixel ratio and threshold screening, and morphological and connected domain processing were performed. Comparing the improved algorithm with the traditional algorithm, the test results show that the improved algorithm can effectively remove the noise and voids, suppress the shadow, avoid the missed detection target, and have a good detection effect.

Research on the Vehicle Detection Technology Based on Improved Burendra Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1733-1736
Author(s):  
Qiang Li ◽  
Dong Shen ◽  
Xiao Kang Wu
Keyword(s):  
Light Intensity ◽  
Real Time ◽  
Difference Method ◽  
Vehicle Detection ◽  
Moving Targets ◽  
Time Performance ◽  
Video Images ◽  
Detection Technology ◽  
Background Difference

To the background establishment in background difference method, a method is proposed to detect moving targets based on the improved Burendra algorithm. This method can accurately detect moving targets in the video images, meanwhile it can respond to the changes of background and light intensity in the scene in time, being with good real-time performance and robustness.

scholarly journals The search of the anisotropy of the primary cosmic radiation by the difference method

2017 ◽  
Vol 145 ◽  
pp. 03003
Author(s):  
Victor Pavlyuchenko ◽  
Romen Martirosov ◽  
Natalia Nikolskaya ◽  
Anatoly Erlykin
Keyword(s):  
Difference Method ◽  
Cosmic Radiation ◽  
Primary Cosmic Radiation ◽  
The Difference

Study and Simulate the Air Pollution Based on the Finite Difference Method

2012 ◽  
Vol 518-523 ◽  
pp. 2820-2824
Author(s):  
Yi Ni Guo ◽  
Yan Zhang ◽  
Jian Wang ◽  
Ye Huang
Keyword(s):  
Finite Element Method ◽  
Air Pollution ◽  
Finite Difference ◽  
Finite Difference Method ◽  
Difference Method ◽  
The Finite Element Method ◽  
Liquid Diffusion ◽  
Continuous Problems ◽  
The Finite Difference Method ◽  
The Difference

The finite difference method that is the finite element method is used to solve the plane continuous problems. In this article, the theory and method of the finite difference method, as well as the application on the boundary problem are introduced. By analyzing the potential flew field equation and liquid diffusion equation, they are discreted using the difference method and the numerical analysis under certain boundary condition is conducted. In air pollution, the smoke in the diffusion is typical planar continuous problems. In this paper, the finite difference method is used to analyse and simulate the spread of the smoke.

scholarly journals Improving Lidar Windshear Detection Efficiency by Removal of “Gentle Ramps”

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1539
Author(s):  
Kai Kwong Hon ◽  
Pak Wai Chan
Keyword(s):  
Detection Efficiency ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Doppler Lidar ◽  
Hit Rate ◽  
Glide Path ◽  
The World ◽  
Alerting System ◽  
The Difference ◽  
Hong Kong International Airport

The Doppler Lidar windshear alerting system at the Hong Kong International Airport (HKIA), the first of its kind in the world, has been in operation since 2006. This paper reports on an enhancement to the automatic windshear detection algorithm at HKIA, which aims at filtering out alerts associated with smoother headwind changes spread over longer distances along the aircraft glide path (called “gentle ramps”) which may nonetheless exceed the well-established alerting threshold. Real-time statistics are examined over a 46-month study period between March 2016 and December 2019, covering a total of 2,017,440 min and over 1500 quality-controlled pilot reports of windshear (PIREP). The “gentle ramp removal” (GRR) function is able to effectively cut down the alert duration over the 5 major runway corridors, inclusive of both landing and take-off, which together account for over 98% of the PIREP received at HKIA during the study period. In all 5 runway corridors this is achieved with a proportionately smaller decrease—even with no changes in 2 cases—in the hit rate, highlighting the efficiency of the GRR function. The difference in statistical behaviour across the runway corridors also echo literature findings about the differences in length scale of wind disturbances at different locations within HKIA. This study serves as a unique documentation of the state-of-the-art in operational Lidar windshear detection and can provide useful reference to airports and aviation meteorologists around the world.

scholarly journals Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

scholarly journals Urban Change Detection in TerraSAR Image Using the Difference Method and SAR Coherence Coefficient

2018 ◽  
Vol 11 (3) ◽  
pp. 18-23 ◽  
Author(s):  
Zhang Xuedong ◽  
◽  
Liu Wenxi ◽  
He Shuguang ◽  
◽  
...  
Keyword(s):  
Change Detection ◽  
Difference Method ◽  
Urban Change ◽  
The Difference
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