An improved frame difference method for moving target detection

2017 ◽  
Author(s):  
Wenchao Huang ◽  
Yake Kang ◽  
Song Zheng
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Moving Target ◽  
Moving Target Detection ◽  
Frame Difference

scholarly journals Intelligent Monitoring Method of Short-Distance Swimming Physical Function Fatigue Limit Mobile Calculation

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Jianxia Yin ◽  
Shimeng Huang ◽  
Lei Lei ◽  
Jing Yao
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Detection Method ◽  
Classification Problem ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Monitoring Method ◽  
Intelligent Monitoring ◽  
Frame Difference

The detection and classification of moving targets have always been a key technology in intelligent video surveillance. Current detection and classification algorithms for moving targets still face many difficulties, mainly because of the complexity of the monitoring environment and the limitations of target characteristics. Therefore, this article conducts corresponding research on moving target detection and classification in intelligent video surveillance. According to the Gaussian Mixture Background Model and Frame Difference Method, this paper proposes a moving target detection method based on GMM (Gaussians Mixture Model) and Frame Difference Method. This method first proposes a new image combination algorithm that combines GMM and frame difference method, which solves the problems of noise and voids inside the target caused by the fusion of traditional GMM and frame difference method. The moving target detection method can effectively solve the problems of incomplete moving target detection, target internal gap, and noise, and it plays a vital role in the subsequent moving target classification process. Then, the method adds image inpainting technology to compensate the moving target in space and obtain a better target shape. The innovation of this paper is that in order to solve the multiobject classification problem, a binary tree decision support vector machine based on statistical learning is constructed as a classifier for moving object classification. Improve the learning efficiency of the classifier, solve the competitive classification problem of the traditional SVM, and increase the efficiency of the mobile computing intelligent monitoring method by more than 70%.

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 Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

2021 ◽  
Vol 13 (16) ◽  
pp. 3291
Author(s):  
Zhihua He ◽  
Zihan Li ◽  
Xing Chen ◽  
Anxi Yu ◽  
Tianzhu Yi ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Detection ◽  
State Of The Art ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Moving Target ◽  
Moving Target Detection ◽  
Frame Difference ◽  
The Difference ◽  
Distinct Features

Video synthetic aperture radar (VideoSAR) can detect and identify a moving target based on its shadow. A slowly moving target has a shadow with distinct features, but it cannot be detected by state-of-the-art difference-based algorithms because of minor variations between adjacent frames. Furthermore, the detection boxes generated by difference-based algorithms often contain such defects as misalignments and fracture. In light of these problems, this study proposed a robust moving target detection (MTD) algorithm for objects on the ground by fusing the background frame detection results and the difference between frames over multiple intervals. We also discuss defects that occur in conventional MTD algorithms. The difference in background frame was introduced to overcome the shortcomings of difference-based algorithms and acquire the shadow regions of objects. This was fused with the multi-interval frame difference to simultaneously extract the moving target at different velocities while identifying false alarms. The results of experiments on empirically acquired VideoSAR data verified the performance of the proposed algorithm in terms of detecting a moving target on the ground based on its shadow.

Applying the Technology of Moving Target Detection in Missile Training Equipment

2017 ◽  
Vol 31 (06) ◽  
pp. 1750017 ◽  
Author(s):  
Guoqing Zhou ◽  
Xinghui Wang ◽  
Xinrong Li
Keyword(s):  
Target Detection ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Video Frame ◽  
Moving Target ◽  
Moving Target Detection ◽  
Mapping Algorithm ◽  
Frame Difference ◽  
Detection Technology ◽  
System Maintenance

The process of missile launch training was confined to the virtual scene in the past. So, cooperating with an artillery college, the group makes the moving target detection technology to be applied in missile training equipment, so as to make the training apply to the field operations. This paper presents the frame difference mapping algorithm, which is used to detect the moving target in the background of moving video frame. According to the target region which is given out by the system in the graphical interface, the students do the launching missile training. The moving target detection algorithm which is provided with the low complexity and the high accuracy, i.e. proposed by the paper, is based on Gauss mixture model and frame difference mapping. The mechanism of layered-graphics and the message agent which makes the modules in the system be independent of each other are used in the system designing. So, the module coupling degree in terms of this mechanism is lower than before. This mechanism brings convenience to system maintenance and upgrade, especially for the system’s transplanting to the real missile launch system in future.

Variable Background Frame Difference Technology Research

2014 ◽  
Vol 556-562 ◽  
pp. 4742-4745
Author(s):  
Ju Bao Qu
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
High Speed ◽  
Moving Objects ◽  
Moving Target ◽  
Technology Research ◽  
Complex Environment ◽  
Frame Difference ◽  
Speed Change ◽  
Real Time Tracking

When the target and background in the high speed change, moving target detection. The traditional easily lost, not accurate. This paper presents a variable background frame difference method, and makes use of the MeanShift tracking algorithm simulation application. The method can detect moving objects in complex environment, and real-time tracking, can quickly and accurately detect and track when the background and target are scale, rotation, no rules of large displacement changes.

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