scholarly journals An Improved ViBe Algorithm of Moving Target Extraction for Night Infrared Surveillance Video

2021 ◽  
Vol 15 (12) ◽  
Keyword(s):  
Moving Target ◽  
Surveillance Video ◽  
Target Extraction ◽  
Vibe Algorithm

The infrared moving target extraction and fast video reconstruction algorithm

2019 ◽  
Vol 97 ◽  
pp. 85-92 ◽  
Author(s):  
Shi Qiu ◽  
Ying Tang ◽  
Yun Du ◽  
Song Yang
Keyword(s):  
Reconstruction Algorithm ◽  
Moving Target ◽  
Target Extraction ◽  
Video Reconstruction

Moving-target extraction algorithm for selective coding

1996 ◽  
Author(s):  
Chan-Sik Kim ◽  
Sang-Yeon Kim ◽  
Jong-Bae Lee ◽  
Seong-Dae Kim
Keyword(s):  
Moving Target ◽  
Extraction Algorithm ◽  
Target Extraction

A moving target extraction algorithm based on the fusion of infrared and visible images

2019 ◽  
Vol 98 ◽  
pp. 285-291 ◽  
Author(s):  
Shi Qiu ◽  
Junsong Luo ◽  
Song Yang ◽  
Meiyang Zhang ◽  
Wei Zhang
Keyword(s):  
Moving Target ◽  
Extraction Algorithm ◽  
Target Extraction ◽  
Visible Images

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.

Research of AForge.NET in Motion Video Detection

2014 ◽  
Vol 496-500 ◽  
pp. 2150-2153 ◽  
Author(s):  
Cheng Ying Gong ◽  
Hui He
Keyword(s):  
Moving Objects ◽  
Detection Method ◽  
Threshold Value ◽  
Moving Target ◽  
Location Tracking ◽  
Surveillance Video ◽  
Subtraction Method ◽  
Optical Flow Method ◽  
Adjacent Frame ◽  
Motion Video

In general, moving target detection method has the background subtraction method,the adjacent frame difference method and the optical flow method. In order to quickly and accurately detect the moving targets of surveillance video , expand the location tracking study.This paper describes the detection method in real-time vehicle moving target,used AForge.NET library and based on the absolute of image gray difference is greater than the taken threshold value. Implementation results show that this method can effectively and quickly detect moving objects in video sequences.

Moving Target Classification in Road Monitoring Based on Multi-Feature Fusion

2013 ◽  
Vol 401-403 ◽  
pp. 1208-1211
Author(s):  
Lin Wu ◽  
Xiao Pei Wu ◽  
Juan Xu
Keyword(s):  
Aspect Ratio ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
Moving Target ◽  
Dynamic Feature ◽  
Connected Component ◽  
Target Classification ◽  
Connected Component Labeling ◽  
Ratio Variation ◽  
Target Extraction

A method for moving target classification in road monitoring based on multi-feature fusion is presented in this paper. In this method, connected component labeling and merging combined with morphology are used to achieve the target extraction. Static features in moving target are extracted. To improve the low classification accuracy, a dynamic feature, lower thirds aspect ratio variation (also named as LTVar), is proposed and added. The recognition ratio obtains the relative increasing of 3.1% compared with the static features.

Ground Moving Target Extraction in a Multichannel Wide-Area Surveillance SAR/GMTI System via the Relaxed PCP

2013 ◽  
Vol 10 (3) ◽  
pp. 617-621 ◽  
Author(s):  
He Yan ◽  
Robert Wang ◽  
Fei Li ◽  
Yunkai Deng ◽  
Yabo Liu
Keyword(s):  
Wide Area ◽  
Moving Target ◽  
Target Extraction ◽  
Area Surveillance ◽  
Wide Area Surveillance
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