scholarly journals Design of Sports Image Contour Feature Acquisition System Based on the Background Subtraction Method

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Zhichao Xiong
Keyword(s):  
Difference Method ◽  
Main Idea ◽  
Image Method ◽  
Moving Target ◽  
Fusion Algorithm ◽  
Complex Scene ◽  
Difference Image ◽  
Feature Acquisition ◽  
And Behavior ◽  
Background Difference

Moving target detection (MTD) is one of the emphases and difficulties in the field of computer vision and image processing. It is the basis of moving target tracking and behavior recognition. We propose two methods are improved and fused, respectively, and the fusion algorithm is applied to the complex scene for MTD, so as to improve the accuracy of MTD in complex and hybrid scenes. Using the main idea of the three-frame difference image method, the background difference method and the interframe difference method are combined to make their advantages complementary to overcome each other’s weaknesses. The experimental results show that the method can be well adapted to the situation of periodic motion interference in the background, and it can adapt to the situation of sudden background changes.

A Motion Image Detection Method Based on the Inter-Frame Difference Method

2014 ◽  
Vol 490-491 ◽  
pp. 1283-1286 ◽  
Author(s):  
Yuan Hang Cheng ◽  
Jing Wang
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Background Subtraction ◽  
Vision System ◽  
Robot Vision ◽  
Main Idea ◽  
Image Method ◽  
Detection Model ◽  
Difference Image ◽  
Frame Difference

Mobile robot vision system based on image information on environment, to make it automatic separation from obstacles and achieve precise mathematical description of obstacles, we construct detection model which combined by the frame difference method and background subtraction for target detection, comprehensive utilization of the main idea of three frame difference image method, the background subtraction and frame difference method combined to complement each other, thereby overcoming each other's weaknesses and improving the effect of target detection, experiment results show that this method can effectively improve the efficiency of target detection.

Moving Target Detection Based on the Improved Gaussian Mixture Model Background Difference Method

2012 ◽  
Vol 482-484 ◽  
pp. 569-574
Author(s):  
Hong Liang Wang ◽  
Jin Qi Wang ◽  
Hai Fei Ding ◽  
Yang Wen Huang ◽  
Pan Liu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Target Detection ◽  
Difference Method ◽  
Gaussian Mixture ◽  
Moving Target ◽  
Moving Target Detection ◽  
Current Frame ◽  
Model Background ◽  
Background Difference

Gaussian mixture model background difference method is an effective method to achieve the moving target detection. According to its deficiencies of accuracy, speed and other aspects, this paper presents an improved Gaussian mixture model background difference method. Firstly, use three-frame difference method to detect the alterant area rapidly by the advantages of accuracy and fast speed. Then, use the area method to judge the results, and determine whether it is need for target extraction of the current frame by Gaussian mixture model background method, which can reduce the time of object detecting and background modeling. Meanwhile, the update strategies of the Gaussian mixture model background is improved, which can further enhance the detective accuracy and speed for the large and slow moving targets.

Moving Target Detection Based on Improved YUV_Vibe Fusion Algorithm

2018 ◽  
Vol 55 (11) ◽  
pp. 111002
Author(s):  
谢申汝 Xie Shenru ◽  
叶生波 Ye Shengbo ◽  
杨宝华 Yang Baohua ◽  
王学梅 Wang Xuemei ◽  
何红霞 He Hongxia
Keyword(s):  
Target Detection ◽  
Moving Target ◽  
Fusion Algorithm ◽  
Moving Target Detection

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 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.

scholarly journals Detection and Adaptive Video Processing of Hyperopia Scene in Sports Video

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qingjie Chen ◽  
Minkai Dong
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Motion Detection ◽  
Difference Method ◽  
Detection Algorithm ◽  
Moving Target ◽  
Target Area ◽  
Moving Target Detection ◽  
Target Extraction ◽  
Motion Video

In the research of motion video, the existing target detection methods are susceptible to changes in the motion video scene and cannot accurately detect the motion state of the target. Moving target detection technology is an important branch of computer vision technology. Its function is to implement real-time monitoring, real-time video capture, and detection of objects in the target area and store information that users are interested in as an important basis for exercise. This article focuses on how to efficiently perform motion detection on real-time video. By introducing the mathematical model of image processing, the traditional motion detection algorithm is improved and the improved motion detection algorithm is implemented in the system. This article combines the advantages of the widely used frame difference method, target detection algorithm, and background difference method and introduces the moving object detection method combining these two algorithms. When using Gaussian mixture model for modeling, improve the parts with differences, and keep the unmatched Gaussian distribution so that the modeling effect is similar to the actual background; the binary image is obtained through the difference between frames and the threshold, and the motion change domain is extracted through mathematical morphological filtering, and finally, the moving target is detected. The experiment proved the following: when there are more motion states, the recall rate is slightly better than that of the VIBE algorithm. It decreased about 0.05 or so, but the relative accuracy rate increased by about 0.12, and the increase ratio is significantly higher than the decrease ratio. Departments need to adopt effective target extraction methods. In order to improve the accuracy of moving target detection, this paper studies the method of background model establishment and target extraction and proposes its own improvement.

scholarly journals Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zheng Zhang ◽  
Cong Huang ◽  
Fei Zhong ◽  
Bote Qi ◽  
Binghong Gao
Keyword(s):  
Machine Vision ◽  
Target Detection ◽  
Target Object ◽  
Tracking Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking ◽  
Swimming Motion ◽  
Behavior Tracking ◽  
And Behavior

This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.

scholarly journals Design of Wireless Security Surveillance System in Remote Unattended Wells

2019 ◽  
Vol 15 (07) ◽  
pp. 4
Author(s):  
Jian Du ◽  
Min Guang Meng ◽  
Chao Yang
Keyword(s):  
Video Surveillance ◽  
Difference Method ◽  
Surveillance System ◽  
Gaussian Model ◽  
Comparison Results ◽  
Mobile Target ◽  
Security Surveillance ◽  
Background Difference ◽  
Detection Effect ◽  
Better Than

this paper analyzes the characteristics of the frame difference method and the background difference method in extracting the moving target under the condition of fixed camera, and puts forward the improved background difference method in combination with the advantages of the two. Meanwhile, background modeling is proposed to reduce the influence of external environment on mobile target detection. The comparison results show that the moving object detection effect based on the improved background difference method is better than the background difference method based on the mixed Gaussian model. The relevant test result of the simulation experimental platform shows that, the designed unattended security video surveillance system has feasibility. It is of great significance to improve the safety of the traditional video surveillance by the operator, to prevent people deliberately invasion, and to ensure the production and property safety of unattended well site.

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