Moving Target Detection Using Inter-Frame Difference Methods Combined with Texture Features and Lab Color Space

2019 ◽  
Author(s):  
Fan Gao ◽  
Yonggang Lu
Keyword(s):  
Target Detection ◽  
Color Space ◽  
Texture Features ◽  
Moving Target ◽  
Moving Target Detection ◽  
Frame Difference ◽  
Lab Color Space ◽  
Difference Methods ◽  
Inter Frame

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 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.

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%.

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