Video Image Processing Technology Research Articles

Research on New Greenable Class Gravity Retaining Wall Structure Technology Based on Video Monitoring

As the most common geological disaster problem in mines, slope geological disasters have become a focus of research, along with the difficulty of mine safety and ecological environment protection together with the ecological restoration of open-pit mines. At present, a large number of slope-retaining wall structures lack research on safety monitoring, real-time acquisition, and intelligent early warning. Therefore, this paper combines cement-modified loess with gravity retaining wall structures and puts forward a new type of greening gravity retaining wall structure. From the perspective of “the Internet of Things + construction”, a video monitoring system is established to monitor the retaining wall structure in real time. Finally, based on video image processing technology, the deformation of the retaining wall surface is identified and the inclination angle of the wall surface is calculated, so as to improve the real-time and intelligent monitoring of the new greening gravity retaining wall. The results show that the new greening gravity retaining wall based on video monitoring proposed in this paper has the characteristics of a gravity retaining wall and ecological retaining wall, which are conducive to improving the real-time and intelligent monitoring of the new greening gravity retaining wall. Cement-modified loess is used as the planting matrix, and the cement mixing ratio should not exceed 10%. Considering the requirements of economy and shear strength, the cement mixing ratio should be selected from 5% to 12%.

Application of video image processing in sports action recognition based on particle swarm optimization algorithm

The existing sports training methods are mainly aimed at the sports field environment. The traditional sports training is only based on the coaches' visual inspection and combined with their own experience to put forward suggestions, which is relatively inefficient, thus limiting the rise of athletes' sports training level to a certain extent. Based on this background, combining traditional physical education teaching methods with video image processing technology, especially using particle swarm optimization algorithm, can promote the application of human motion recognition technology in physical training. This paper mainly investigates the optimization process of particle swarm optimization algorithm and discusses the development of particle swarm optimization algorithm; Secondly, through video decoding, image noise removal, video enhancement and other methods, complete video image processing and establish the structure of the manikin to achieve the collection of key points of the target, and then collect relevant data with experimental methods The results show that the motion recognition system proposed in this paper can effectively detect the changes of athletes' sampling point path, and can be compared with standard movements, which has a good auxiliary role. With the application of video image processing technology in sports training becoming more and more common, athletes can analyze their training videos in a more intuitive way and find out shortcomings, so as to improve the training effect. This paper studies particle swarm optimization algorithm and applies it to the field of video image processing, which promotes the development of sports action recognition technology based on video processing.

Design and implementation of moving object detection based on Zynq

In recent years, video image processing technology has made rapid progress. Some algorithms related to moving target detection and tracking, relying on the rich resources of computers, make the implementation of these algorithms based on the PC platform has been relatively mature, but there are problems such as poor real-time performance, high power consumption, resource consumption. The Zynq hardware platform has both FPGA, which is good at parallel operation, and ARM core, which is good at control, which can effectively implement the algorithm transplantation of moving target detection and effectively improve the above shortcomings. The system takes Zynq hardware platform as the core unit, which is composed of video image acquisition module, image processing module, information transmission module, DDR cache module and display module. The PL side of Zynq implements video image acquisition, gray conversion, frame difference algorithm, expansion corrosion algorithm. The PS terminal mainly completes the configuration of IIC camera and VDMA. The output module finally realizes the output of HDMI through protocol conversion. This system has wide application value in moving target detection.

The Application of Real-Time Video Processing Technology in English Culture Teaching

In order to improve the effect of English culture teaching, this paper combines the real-time video image processing technology to construct an English culture teaching system. Moreover, this paper selects the image processing based on octree structure with the advantages of full 3D modeling and being updatable, flexible, and compact by comparison. At the same time, this paper analyzes the existing Gaussian-based inverse sensor model that can be used for monocular vision and improves the measurement inverse sensor model for stereo vision published by Andert. In addition, this paper deeply analyzes the influence of key parameters of the improved measurement inverse sensor model and realizes the real-time octree occupancy grid map of the improved measurement inverse sensor model. Finally, after improving the real-time video processing technology, this paper evaluates the application effect of the method proposed in this paper in English culture teaching. The experimental research results show that the real-time video processing technology proposed in this paper has a good effect in English culture teaching, and it can be applied to English culture teaching to improve the effect of English culture teaching.

Motion Video Image Based on Sensor and Multiprocessing Technology in Track and Field Sports

In order to use motion video image processing technology to better guide and evaluate athletes’ technical and tactical ability, this paper first constructed a motion video image acquisition system. Secondly, a fuzzy kernel estimation method for uniform linear motion based on cepstrum property is proposed. Then, the motion blur areas to be processed are selected, and only these areas are deblurred. Finally, the effective removal of local motion blur is realized, and a clear scene image is obtained. Then, the maximum value of the total entropy of the image is calculated by using the information entropy theory, and the particle swarm optimization algorithm is introduced to find the maximum threshold of image segmentation. Finally, small wave optical flow estimation algorithm and rectangular window scanning algorithm across scales of motion image target detection algorithm are proposed, to not only solve the traditional optical flow estimation for fast moving object detection accuracy but also improve the efficiency of the optical flow computation. Compared to many kinds of algorithms, this paper proposed an algorithm that can improve the accuracy of moving target detection and measurement accuracy. And, the detection accuracy of the proposed algorithm is up to 86.5%. The estimated accuracy was as high as 65%. The segmentation accuracy is up to 95%.

Vehicle Detection Algorithm Based on Embedded Video Image Processing in the Background of Information Technology

As the main means of transportation for urban residents, the number of motor vehicles is increasing year by year. With the continuous development of society and the gradual improvement of people’s quality of life, automobiles have gradually become an indispensable means of transportation in people’s lives, resulting in increased traffic flow. However, the old traffic system is still unable to cope with the rapid growth of traffic pressure, and traffic congestion and various accidents occur frequently, which is a huge test for the contemporary intelligent traffic system. With the gradual development of society, more and more researchers are devoted to intelligent transportation systems, which make the development of target detection technology based on video image processing more and more rapid. The primary problem in embedding digital video in applications is that the complexity of video encoding and decoding far exceeds that of simple image and audio compression and decompression. Digital video can take various forms and formats. Developers need to support complex configurations and various aspects, such as different resolutions/display sizes, different bit rates, real-time issues, and even the reliability of the video source. Intelligent transportation achieved a lot of results. However, there are still some deficiencies in precision and robustness. At the same time, the improvement of video image processing technology gives us a new idea. To further improve the intelligent traffic system, provide accurate data information for all departments, and improve the traffic situation, this study, based on video image processing technology, combined with the three-frame difference algorithm, calculates and studies the data of illegal parking at a certain intersection. The calculated false detection rates for Y2 are 1.1%, 0.9%, and 2.4%, and the leakage rates for Y1 are 2.4%, 1.9%, and 4.7%, respectively. This shows that the algorithm has high accuracy for vehicle parking detection data and can collect information quickly and effectively. Applying the algorithm to the detection of other vehicles can provide efficient services for relevant traffic departments and public security departments and relieve traffic pressure. The image processing technology is a process of analyzing and processing images through certain computer technology to achieve the desired results. The scheme in the article realizes background extraction, image filtering, image binarization, morphological transformation, vehicle detection and segmentation, shadow detection, etc.

A Study on the Optimization Simulation of Big Data Video Image Keyframes in Motion Models

In this paper, the signal of athletic sports video image frames is processed and studied according to the technology of big data. The sports video image-multiprocessing technology achieves interference-free research and analysis of sports technology and can meet multiple visual needs of sports technology analysis and evaluation through key technologies such as split-screen synchronous comparison, superimposed synchronous comparison, and video trajectory tracking. The sports video image-processing technology realizes the rapid extraction of key technical parameters of the sports scene, the panoramic map technology of sports video images, the split-lane calibration technology, and the development of special video image analysis software that is innovative in the field of athletics research. An image-blending approach is proposed to alleviate the problem of simple and complex background data imbalance, while enhancing the generalization ability of the network trained using small-scale datasets. Local detail features of the target are introduced in the online-tracking process by an efficient block-filter network. Moreover, online hard-sample learning is utilized to avoid the interference of similar objects to the tracker, thus improving the overall tracking performance. For the feature extraction problem of fuzzy videos, this paper proposes a fuzzy kernel extraction scheme based on the low-rank theory. The scheme fuses multiple fuzzy kernels of keyframe images by low-rank decomposition and then deblurs the video. Next, a double-detection mechanism is used to detect tampering points on the blurred video frames. Finally, the video-tampering points are located, and the specific way of video tampering is determined. Experiments on two public video databases and self-recorded videos show that the method is robust in fuzzy video forgery detection, and the efficiency of fuzzy video detection is improved compared to traditional video forgery detection methods.

Value Analysis of Video Image Multiprocessing Based on Vision Sensor in the Field of Football Scientific Research

The development and application of mobile portable video capture and image processing systems, as well as the development and improvement of computer multimedia courseware technology have made image processing technology widely used in football project scientific research services. This research mainly discusses the value of video image multiprocessing based on vision sensors in the field of football scientific research. The development and conduct of this research work require the application of sports video image processing technology software to conduct scientific experimental research and analysis on the athletes’ technical and tactical level, evaluate the athletes’ technical and tactical performance through qualitative and quantitative tests, and give different sports items at the same time. Humans can exchange information with the outside world through vision, hearing, and language and can express the same meaning in different ways. However, current intelligent machines or computers require programs to be written in strict accordance with various machine languages. Only in this way can the machine run. In order to enable more people to use complex machines, it is necessary to change the past situation where people adapt to machines. Instead, let the machine adapt to people’s habits and requirements and exchange information with people in the way people are used to, that is, let the machine have the ability to see, hear, and speak. At this time, the machine must have the ability of logical reasoning and decision-making. Use sports video image processing technology and use examples to study and explore practical methods and means that are most in line with the scientific work of the project, and build the most scientific and useful sports video image processing system.. The CCD visual image sensor is used for signal acquisition and image processing when the motion video image processing system is established. Through logical analysis and induction, discuss and prove the intuitiveness, practicability, efficiency, and scientificity of the application of video image processing technology in the field of football scientific research. Finally, in the mathematical statistics method, the statistical software Excel 2003 in the office system is used to perform statistical analysis on all data results and analyze and compare the key kinematic parameters of some sports events. Analyzing the video images of the 2016 European Cup, the average possession rate of the Welsh team is 48%, and the average pass success rate per game is 82.7%. This research helps to improve the ability of football players to analyze, research, and evaluate sports skills.

3D Mathematical Modelling Technology in Visual Rehearsal System of Sports Dance

Abstract Computer vision technology and video image processing technology in the visual rehearsal of sports dance is a hot research topic. Based on this research background, the thesis uses 3D mathematical modelling technology to interpolate and extract the captured sports and dance movement information to make the final synthesised human animation natural, smooth and lifelike. At the same time, the thesis realises the method of action cohesion through the definition of characteristic action unit attributes and association constraints. Then, it applies it to the visual rehearsal system of sports dance. Finally, the analysis of experimental results proves that the proposed method can improve the precision and recall of rehearsal.

Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology

In order to improve the video image processing technology, this paper presents a moving object detection and tracking algorithm based on computer vision technology. Firstly, the detection performance of the interframe difference method and the background difference model method is compared comprehensively from both theoretical and experimental aspects, and then the Robert edge detection operator is selected to carry out edge detection of the vehicle. The research results show that the algorithm proposed in this paper has the longest running time per frame when tracking a moving target, which is about 2.3 times that of the single frame running time of the CamShift algorithm. The algorithm has high running efficiency and can meet the requirements of real-time tracking of a foreground target. The algorithm has the highest tracking accuracy, the time consumption is reduced, and the error of the tracking frame deviating from the real position of the target is the least.

Comparing the Accuracy of Artificial Neural Networks in Estimating the Weight of Cobb, Ross, and Arbor Acres Chicks using Video Image Processing Technology

This study aimed to compare the accuracy of artificial neural networks (ANNs) in estimating the weight of broilers using video image processing technology. A total number of 900 broiler chicks from three different strains (Ross 308, Cobb 500, and Arbor Acres) were fed on commercial diets and reared under standard situations for 42 days. Thirty male and female chicks from each strain were weighed randomly using digital scales every day while simultaneously filmed from top view using a Xenon camera (2MP 1080IP lens). In image processing, digital images initially were extracted from films and then each image was processed using GUI of MATLAB software. Sixteen morphological features extracted from images that significantly correlated with the chicks' weight, were used as inputs of the artificial neural network, and multilayer perceptron ANN was trained to predict the weight of chickens of each strain via an error propagation algorithm. The procedure was the same for all three strains. The accuracy of ANN models to predict the weight of chicks were 98.4% (with an average error of 7.9 g), 99.54% (with an average error of 0.37 g), and 99.67% (with an average error of 2 g) for Ross, Cobb, and Arbor Acres strains, respectively. In conclusion, a comprehensive intelligent model can be designed based on artificial neural networks and video image processing technology to estimate the weight of broiler chickens regardless of their strain type.

Simulation of tennis serve behavior based on video image processing and wireless sensor technology

Video-based human motion analysis is an important research direction in the field of computer vision. It detects moving objects from video sequences, extracts key parts of the human body, and obtains useful information for human movements. In this paper, the joints of the teeing arm are first color-coded. The tennis teeing video is collected by a high-speed camera. The coordinates of the tacking points in each frame are used instead of the knuckles to study the trajectory of the teeing arm. In the process of video processing, after constructing a dictionary for a series of noise maps, the sparse representation idea was used to reconstruct an interference-free service diagram, and a mixture of Gaussian background modeling was used to extract the foreground of the motion. After obtaining the motion foreground, the marker points are extracted through the color features, and binarization operations are performed on the marker points. Next, the outline of the marker points is searched, the outline is surrounded by the minimum circle, and the returned circle center coordinates are used as the joint point coordinates. Taking the trajectory of the shoulder marking point as a research object, a tennis serving model based on an improved support vector machine was established.

Intelligent Control of Urban Lighting System Based on Video Image Processing Technology

The rapid iterative development of information technology, computer technology and Internet of things technology promotes the construction of smart city, and the related concepts of smart city gradually move from theory to practice. As an important part of the development of smart city, the intelligent lighting and intelligent control of urban lighting system become increasingly urgent with the development of the latest Internet of things technology and video image processing technology. Based on the current Internet of things data communication and related processing technology as the background, this article constructs the overall control model of smart city lighting system, including GPRS wireless transmission technology and ZigBee technology. In the core algorithm, this article innovatively presents an improved video image processing technology based on Gaussian mixture algorithm, which mainly describes the background of a specific scene in multi-dimensional model, so as to improve the reliability of the whole scene judgment. At the level of noise suppression, the video image processing algorithm proposed in this article designs interference noise suppression technology based on the principle of mean filtering, so as to achieve the accuracy and integrity of urban traffic scene extraction. In order to solve the security problems of the whole video image processing technology in information extraction, storage and analysis, this article adds the electromagnetic information leakage prevention algorithm in the video image processing technology, which mainly processes the video image at the source, so as to reduce the electromagnetic radiation of the overall information. Based on this, this article completed the construction of the software and hardware of the city intelligent lighting system. On this basis, the software and hardware construction of urban intelligent lighting system is completed. At the same time, the experiment was carried out in a small-scale scene of a city. The experimental results show that the intelligent urban lighting system based on video image processing technology is feasible and can achieve the desired effect. At the same time, the lighting energy consumption level can be reduced by about 30%, and the corresponding failure rate can be reduced by about 30%.

Development and Application of Tunnel Apparent Distress Monitoring System Based on Video Image Analysis

With a lack of complete system and hardware equipment for long-term monitoring of distresses in an operating tunnel to address theoretical and technical issues, it is necessary to develop an IoT-based integrated system for long-term monitoring of lining crack, water leakage and spalling in an operating tunnel using video image processing technology and technique in conjunction with computer programming languages. Use of video image processing methods of this monitoring system allows rapid, accurate identification of distresses and the extent thereof. It is concluded that linear array CCD is the best option for engineering surveillance cameras since such CCD is able to accurately capture both static and dynamic targets; the use of higher-definition cameras provides for tiny target identification under remote monitoring conditions and the addition of temporary storage module for field data.

Key Algorithms of Video Target Detection and Recognition in Intelligent Transportation Systems

With the popularization of video detection and recognition systems and the advancement of video image processing technology, the application research of intelligent transportation systems based on computer vision technology has received more and more attention. It comprehensively utilizes image processing, pattern recognition, artificial intelligence and other technologies. It also involves processing and analyzing the video image sequence collected by the detection system, intelligently understanding the video content and making processing, and dealing with various problems such as accident information judgment, pedestrian and vehicle classification, traffic flow parameter detection, and moving target tracking. It promotes intelligent transportation systems to be more intelligent and practical, and provides comprehensive, real-time traffic status information for traffic management and control. Therefore, the research on the method of traffic information detection based on computer vision has important theoretical and practical significance. The detection and recognition of video targets is an important research direction in the field of intelligent transportation and computer vision. However, due to the background complexity, illumination changes, target occlusion and other factors in the detection and recognition environment, the application still faces many difficulties, and the robustness and accuracy of detection and recognition need to be further improved. In this paper, several key problems in video object detection and recognition are studied, including accurate segmentation of target and background, shadow in complex scenes; accurate classification of extracted foreground targets; and target recognition in complex background. In response to these problems, this paper proposes a corresponding solution.

Pig Detection Algorithm Based on Sliding Windows and PCA Convolution

An accurate and rapid pig detection algorithm based on video image processing technology can be helpful to identify abnormal pigs and take timely measures to reduce the incidence of diseases. In order to solve the problems of low computational efficiency and low precision in pig detection algorithm based on sliding windows, this paper proposed a simple and efficient pig detection algorithm. A two-level support vector machine model was trained to calculate the probabilities of sliding windows by using gradient and gray distribution features of pigs. The principal component analysis convolution kernels were trained to extract foreground and background features of pig images. The support vector machine was used to classify sliding windows to obtain windows where pigs were located, and the non-maximum suppression algorithm was used to eliminate redundant windows to complete the target detection. The experiments showed that the proposed algorithm blending gradient and gray distribution features had a higher recall rate than the BING algorithm. The recall rate was up to 99.21% using 500 windows. The classification accuracy of sliding windows in this paper was 95.21%, which was higher than that of the PCANet. By calculating the omission detection rate, the misdetection rate, and the average detection time, it can be seen that in the detection methods of the proposed algorithm, BING + PCANet, faster rcnn and yolo, the performance of the proposed algorithm was optimal.

WAVE INDUCED CURRENT MONITORING USING UAV-BASED PHOTOGRAMMETRY

In recent years, due to the development of hardware and video image processing technology, image analysis technique using a wide coastal coastline, it made it possible to analyze the wave runup and swash, current and so on. However, in order to obtain the image information of a large area requires a high-resolution imaging at a high position, however, to the recording technique used in the past, there is a limit of height and resolution. Thus, recently, on the basis of remote sensing techniques, or build spatial information technology using UAV (Unmanned Aerial Vehicle) system, and the research and utilization in various fields it increased. Small UAV platforms is becoming low-cost, miniaturized hyperspectral imaging technology. The direct measurement of wave induce current is tested using image analysis techniques using sea dye maker and UAV. Specially, rip currents are approximately shore normal seaward flows which are strong, localized and rather narrow. In this method, UAV at a region with frequent wave induced current occurrence, and images are continuously obtained in real time. This research investigates the development of wave induced currents using images in East coast of Korea.

Parameter Modification Guiding Optimization Design and Tests of a Rotary Transplanting Mechanism for Rice Plug Seedlings

A parameter modification guiding optimization method combined with fuzzy optimization was applied to improve the design of a rice plug seedling transplanting mechanism using a planetary gear train with elliptical gears and an incomplete non-circular gear. The laboratory kinematics was examined, and seedling transplanting tests were conducted. Based on the work principle analysis of the transplanting mechanism for rice plug seedlings, numerical kinematic objective functions were established using fuzzy objectives requirements such as transplanting trajectory and posture. Subordinate functions were determined to describe the objective functions. Then the optimization model of the mechanism was constructed which took the value of the subordinate degree of objective functions as the optimization objectives and the motion interference, height of the transplanting trajectory and mechanical structure as the constraint conditions. Parameter guiding optimization software based on Visual Basic 6.0 was developed independently to determine a set of better parameters for the mechanism, which can reduce dependence on expert experience and improve the efficiency of the design. The mechanism structure was designed, and a physical prototype was manufactured. A high-speed digital video camera and image processing technology were used to perform the kinematics bench tests to obtain the transplanting trajectory and posture of the mechanism. The consistency between the test results and theoretical results shows that the optimized mechanism can satisfy the transplanting requirements and that the design method and results are also accurate. Finally, rice seedling transplanting tests were conducted to verify the transplanting performance of the mechanism. The mechanism extracted 200 seedlings per minute per row with a success ratio of 95.89%; the mechanism also displayed a good effect in seedling pushing, showing that the mechanism could be applied in a practical machine.

Walking Posture Analysis of Pigs Based on Star Skeleton Model

In order to make better use of video image processing technology to monitor and analyze behaviors of animals, a new walking posture analysis method based on star skeleton is proposed. Firstly, a pig target is detected and its boundary is extracted from the images. Secondly, according to the target’s boundary the center of mass is computed and the curve of distance between contour points and center of mass is draw. The extremums of the above curve are computed to obtain the key contour points from the curve. The stick model of pigs is built by connecting the key contour points with its center of mass. Finally, the gaits of pigs are recognized by analyzing cyclic motion of the key contour points in the stick model. More specifically, the foreleg walking frequency of pigs could be calculated using the spectrum analysis method. The experiments results indicate that this method has higher recognition accuracy for the walking gaits of pigs. The relative error of the results is only within 10%. Furthermore, compared with manual monitoring method, it is more objective and convenient. Thus, this algorithm provides a new solution for animal behavior analysis based on video surveillance and image processing.

The System Design of Wireless Video Transmission Based on PTZ Control

Along with the rapid expansion of automation, communication, the Internet of things technology, and video image processing technology, the wireless video transmission technology based on the embedded system is becoming increasingly more mature. In this paper, what has been designed and managed to be perfectly applied is a transmission system of high reliability and convenience which based on the technique of pan-tilt control and integrated the technology of wireless LAN, embedded technology, video transmission technology. The paper briefly introduces the research background of the system, current research status at home and abroad, the development trend and the significance of this design project. What is more is that the article goes through the hardware and software design of the pan-tilt control circuit and software of real-time video transmission in a detailed way. The biggest is that this project has made the real-time video transmission in the local area network came true and you can check the real-time dynamic video images by browsing the web through you mobile phone.