scholarly journals Research on intelligent substation monitoring by image recognition method

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Weijie Tang ◽  
Honggang Chen
Keyword(s):  
Image Recognition ◽  
Difference Method ◽  
Gaussian Mixture ◽  
Moving Target ◽  
Recognition Method ◽  
Active Target ◽  
Frame Difference ◽  
Improved Algorithm

AbstractThis study mainly analyzed the improved three-frame difference algorithm for the identification of active targets in the intelligent substation. The improved three-frame difference algorithm introduced the Gaussian mixture background algorithm on the basis of the traditional three-frame difference method. The Gaussian mixture background algorithm, traditional three-frame difference method, and improved three-frame difference method were tested in the actual substation. The results showed that the improved difference method eliminated the non-target background more thoroughly when recognizing the moving target in the image; in the tested video, the improved algorithm had the highest precision and recall ratios for the active target in the video. To sum up, the improved three-frame difference method can more accurately and effectively identify the active targets in the monitoring video, so as to provide an effective support for the unmanned monitoring of intelligent substation.

Research on Cloud Computing Modeling Based on Fusion Difference Method and Self-Adaptive Threshold Segmentation

2018 ◽  
Vol 32 (06) ◽  
pp. 1859010 ◽  
Author(s):  
Hong Sun ◽  
Shi-Ping Chen ◽  
Li-Ping Xu
Keyword(s):  
Difference Method ◽  
Gaussian Mixture ◽  
Adaptive Threshold ◽  
Background Model ◽  
Moving Target ◽  
Target Area ◽  
Threshold Segmentation ◽  
Update Rate ◽  
The Difference ◽  
Background Area

The traditional Gaussian mixture background model failed to build a reasonable background in complex scenarios, so this paper proposes an improved self-adaptive Gaussian mixture background modeling which integrates the difference method and adaptive threshold segmentation to improve the traditional one. In the proposed model, the difference method is applied to achieve the segmentation of changing area and background area, and different weight update policies are used for different areas. Background area updates background model with a fixed update rate. The changing region is divided into moving target area and background show area with the fusion of adaptive threshold; the background show area has a large update rate, allowing the previously obscured parts to recover rapidly; the moving target area won’t build new Gaussian components for the Gaussian mixture model. Experiments show that the video sequence algorithm with uncertainties of building background model has good adaptability. It helps to improve computing speed to a large extent and it can also respond to the change of the actual scene quickly.

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.

scholarly journals Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Renzheng Xue ◽  
Ming Liu ◽  
Xiaokun Yu
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Moving Objects ◽  
Target Location ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Moving Target ◽  
Size Change ◽  
Detection And Tracking ◽  
Frame Difference

Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

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%.

Fusion of Background Subtraction and Frame Difference in HSV Space for Multi-Object Detection

2013 ◽  
Vol 380-384 ◽  
pp. 3895-3899
Author(s):  
Ya Ne Wen ◽  
Hong Song Li ◽  
Hao Zhou ◽  
Li Ping Tang ◽  
Jun Qi She
Keyword(s):  
Difference Method ◽  
Background Subtraction ◽  
Gaussian Mixture ◽  
Test Results ◽  
Subtraction Method ◽  
False Detection ◽  
Strong Light ◽  
Fusion Frame ◽  
Frame Difference ◽  
Light And Shadow

in order to solve the adverse effects of strong light and shadow on the test results, a fusion frame difference and background subtraction method in the HSV space is used in this paper. By using frame difference method to solve the effect of strong light, but frame difference method can not detect object when the object do not move, the method of background subtraction can detect it, building Gaussian background model in the HSV space can eliminate shadows. Empirical results show that the method of fusion frame difference and background subtraction in the HSV space can get overcome the effect of strong light and shadows. Fusion background subtraction and frame difference method based on establishing a Gaussian mixture model in HSV space can overcome the disadvantages of the frame difference method, at the same time it can also solve the false detection of object which result from the background subtraction method.

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.

Speaker Identity Recognition by Acoustic and Visual Data Fusion through Personal Privacy for Smart Care and Service Applications

2020 ◽  
Vol 64 (4) ◽  
pp. 40404-1-40404-16
Author(s):  
I.-J. Ding ◽  
C.-M. Ruan
Keyword(s):  
Face Detection ◽  
Speaker Recognition ◽  
Visual Information ◽  
Classification Tree ◽  
Gaussian Mixture ◽  
Recognition Method ◽  
Indoor Space ◽  
Identity Recognition ◽  
Visual Identity ◽  
Speaker Classification

Abstract With rapid developments in techniques related to the internet of things, smart service applications such as voice-command-based speech recognition and smart care applications such as context-aware-based emotion recognition will gain much attention and potentially be a requirement in smart home or office environments. In such intelligence applications, identity recognition of the specific member in indoor spaces will be a crucial issue. In this study, a combined audio-visual identity recognition approach was developed. In this approach, visual information obtained from face detection was incorporated into acoustic Gaussian likelihood calculations for constructing speaker classification trees to significantly enhance the Gaussian mixture model (GMM)-based speaker recognition method. This study considered the privacy of the monitored person and reduced the degree of surveillance. Moreover, the popular Kinect sensor device containing a microphone array was adopted to obtain acoustic voice data from the person. The proposed audio-visual identity recognition approach deploys only two cameras in a specific indoor space for conveniently performing face detection and quickly determining the total number of people in the specific space. Such information pertaining to the number of people in the indoor space obtained using face detection was utilized to effectively regulate the accurate GMM speaker classification tree design. Two face-detection-regulated speaker classification tree schemes are presented for the GMM speaker recognition method in this study—the binary speaker classification tree (GMM-BT) and the non-binary speaker classification tree (GMM-NBT). The proposed GMM-BT and GMM-NBT methods achieve excellent identity recognition rates of 84.28% and 83%, respectively; both values are higher than the rate of the conventional GMM approach (80.5%). Moreover, as the extremely complex calculations of face recognition in general audio-visual speaker recognition tasks are not required, the proposed approach is rapid and efficient with only a slight increment of 0.051 s in the average recognition time.

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