Real-time moving target detection based on a nonlinear filter using short-time and medium-time image differences

1999 ◽  
Author(s):  
Holger Lange
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Nonlinear Filter ◽  
Moving Target ◽  
Moving Target Detection ◽  
Medium Time ◽  
Short Time ◽  
Time Image

Model Optimization and Testing on Real-Time Video Moving Target Detection

2014 ◽  
Vol 1006-1007 ◽  
pp. 787-791 ◽  
Author(s):  
Shu Ling Zhang ◽  
Zhi Hong Zhang
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Detection Efficiency ◽  
Color Space ◽  
Simulation Method ◽  
Experimental Simulation ◽  
Moving Target ◽  
Model Optimization ◽  
Threshold Method ◽  
Moving Target Detection

Zhang presented a statistical model of real-time video moving target detection based on Bayesian statistical theory. This article discusses the algorithm parameter selection and detection efficiency of the model by using the experimental simulation method. This article generates a reference background based on unsupervised learning methods, and uses a color space that has a better environmental adaptability to represent the background, and uses dynamic threshold method to classify the results of background subtraction and frame difference. By comparing experimental of different methods, it shows that this algorithm has a greater advantage in terms of accuracy and timeliness.

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.

A Statistical Model for Real-Time Video Moving Target Detection Based on Bayesian Statistics

2014 ◽  
Vol 596 ◽  
pp. 394-397 ◽  
Author(s):  
Zhi Hong Zhang ◽  
Shu Ling Zhang ◽  
Bin Yang ◽  
Xin Bai
Keyword(s):  
Statistical Model ◽  
Real Time ◽  
Target Detection ◽  
Moving Objects ◽  
Relevant Information ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection Model ◽  
Advantages And Disadvantages ◽  
Time Space

Video moving target detection is an important foundation issues in computer vision, based on the analysis of the advantages and disadvantages of each existing moving target detection model, using Bayesian statistical theory as a framework, proposes a statistical model that can detect moving objects in video in real-time. The model combines time, space and color and other relevant information of pixel, divides and extracts Video segmentation’s foreground. By selecting the appropriate reference background can improve the precision and accuracy of the detection.

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