Video Image Processing Research Articles

Moving scene-based video segmentation using fast convolutional neural network integration of VGG-16 net deep learning architecture

Video and object segmentation are considered significant research topics in image-video processing. The techniques and mathematical models involved in detecting and segmenting objects employ several modules of different high-level approaches developed for video analysis, object extraction, classification, and recognition. Moving object detection is important in various applications like video surveillance, moving object tracking. This paper proposes video segmentation of moving scene using fast convolutional neural network with VGG-16 net architecture which improves the accuracy. This developed method based on CNN sparsely represents foreground, background, and segmentation mask, which is used in reconstructing the original images. The feed-forward network-trained videos are applied for object detection in a single image with co-segmentation approach where videos or image collections are required as the input. The segmentation is performed through comparative analysis of real-time DAVIS dataset. The results of the experiment show the efficiency of this proposed method which is tested and compared with the existing techniques such as convolution neural network, [Formula: see text]-nearest neighbors, and artificial neural network by the parameters, namely accuracy, precision, recall, and F1-Score. The proposed technique has been improved in terms of accuracy by 97.8%, precision by 94%, recall by 87.9%, and F1-Score by 83.8%.

Monitoring activity and detecting unexpected events in surveillance footage using Deep CNN

Security is always a primary concern in any domain, because there is an increasing in crime rate and illegal activities. Computer vision learning places a premium on abnormal detection and monitoring, which has numerous applications for dealing with a wide range of issues. We are all aware that there is a high demand for safety protection, personal properties and security, in recent years, video surveillance in systems has become a major focus in people's lives, particularly in government agencies and businesses. The technique we are employing is anomaly detection, which aids in distinguishing various patterns and identifying unusual patterns in a short period of time; these patterns are referred to as outliers. Surveillance videos provide real-time output of unusual events. Anomaly detection in video surveillance entails breaking the process down into three layers: video labelers, image processing, and activity detection. As a result, it detects abnormalities in videos for video surveillance, providing an application by providing accurate results in real-time scenarios. In this proposed work, abnormal events are detected with 98.5 percent accuracy using images and videos. To prevent virus transmissions across the world the government forced to announce the lockdown due to COVID-19 pandemic. As a result, production at manufacturing plants in most areas was halted, resulting in the cessation of all economic activity. There is an even greater need to ensure the safety of youngsters. While there is a pressing need to revive workforce production. The work helps in maintaining social distance and wearing face masks while at work clearly reduces the risk of transmission. Monitor activity decided to identify violations using computer vision (Not Wearing Mask) Real-time alerts that send a trigger and an email with a photo of a rule violation to the appropriate authority as evidence of a rule violation

RBF-Based 3D Visual Detection Method for Chinese Martial Art Wrong Movements

The accuracy of action detection is limited by the extracted action, and there are problems of high processing complexity and low efficiency. Therefore, a three-dimensional visual detection method of martial art wrong action based on RBF is proposed. After noise reduction and weighting processing of martial art action video images, a martial art action 3D visual transformation model is established. According to the 3D visual model, C3D features are used to represent martial art actions. The video is segmented using sparse coding to determine the detection range. RBF neural network model is established, and the combination of the above 3D visual model and network parameters is obtained by sample training to detect martial art wrong actions. The test method of the experimental results shows the detection of the research under the condition of different degrees of precision, an average of at least 5%, and the method of detection of high efficiency and stability.

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.

Reinforcement Learning for Engineering Design Automation

Reinforcement Learning has proven to be capable of solving complex tasks like playing video games, robotics control, speech or image recognition and processing. Transferring Reinforcement Learning into engineering design helps to overcome two current issues of data-driven Design Automation in engineering design. First, dealing with sparse training data resulting from differing design samples. Second, overcoming the limited number of samples in the training data as consequence of short or insufficient product history. To introduce an alternative approach for Design Automation, this contribution studies feasibility, training effort and transferability of Reinforcement Learning in engineering design. The presented method maps engineering requirements and parametric models into learning environments and provides a novel approach for design automation. In addition to that, the contribution summarises the hyperparameters, which design engineers have to set prior to training, and introduces a novel transfer learning concept for Reinforcement Learning in related design tasks. The support is probed by design tasks of performance-oriented bike parts. Case-independent indicators are presented to estimate the case-specific training effort, the effects of hyperparameter variation and the effects of transferring a pretrained agent to related design tasks. Finally, the findings are used to compare Reinforcement Learning to other data-independent Design Automation approaches to assess potential fields of application for Reinforcement Learning in engineering design.

Estimation of the cross-wind speed from turbulent fluctuations of the image of a diffuse target illuminated by a laser beam

The article presents an optical method for assessing the transverse wind, based on the analysis of turbulent distortions of the image of a diffuse target illuminated by a laser beam. The proposed correlation algorithm for processing video images allows one to assess in real time the crosswind speed using one receiving lens when the target is illuminated in the visible or infrared range. An experimental check of the method on the atmospheric path has been carried out. The optical estimates of the integral wind are compared with the data of independent local measurements of six ultrasonic anemometers located along the sensing path.

FPGA-based methods for high-speed processing of video images with large brightness scatter

FPGA-based methods for high-speed processing of video images with large brightness scatter

Vehicle detection and tracking based on video image processing in intelligent transportation system

Vehicle detection and tracking based on video image processing in intelligent transportation system

3D Video Image Processing Effect Optimization Method Based on Virtual Reality Technology

The existing optimization methods have the problem of image edge blur, which leads to a high degree of shadow residue. In order to address this problem, reduce the shadow residual degree, this paper designs a 3D video image processing effect optimization method supported by virtual reality technology. Coding was used to eliminate redundant data in video and eliminate image noise using median filtering. The virtual reality technology detects the image edge and determines the motion offset between the image frames. According to the motion parameters of the camera carrier obtained from the motion estimation, the feature point matching algorithm constructs the video image motion model, and uses the camera calibration technology to set the processing effect optimization mode. It is optimized by perspective projection transformation. Experimental results: the average shadow residual degree of the optimization method and the two existing optimization methods are 3.108%, 6.167% and 6.396% respectively, which proves that the optimization method combined with virtual reality technology has higher practical application value.

Sports Auxiliary Training Based on Computer Digital 3D Video Image Processing.

With the continuous development of social economy, sports have received more and more attention. How to improve the quality of sports has become the focus of research. The computer digital 3D video image processing is introduced in this paper, taking shooting as the starting point, in which computer digitization technology is used to collect images of sequence targets through combining the operation flow of shooting, monitor the results and data of shooting and process 3D video images, conduct the analyze and mine according to the corresponding statistical processing results, and evaluate the corresponding training. The simulation experiment proves that the computerized digital 3D video image processing is effective and can scientifically support sports-assisted training.

A Novel Method to Perceive Self-Vehicle State Based on Vehicle Video by Image Similarity Calculation

Perceiving self-vehicle state based on vehicle information can provide key information for unmanned driving and improve vehicle safety monitoring ability. However, existing studies mainly perceive the vehicle state using out-of-vehicle sensors, positioning systems and in-vehicle sensors, and these methods have their own limitations. In recent years, video image processing has been introduced to transportation research. Despite this and the popularity of vehicle videos, self-vehicle state perception based on vehicle videos captured by the drive recorder remains an unworked area. Therefore, this paper proposed a novel method to perceive self-vehicle state which contains “move” and “stop” by calculating the image similarity of the static region between two adjacent video frames. The static region extraction is based on You Only Look At CoefficenTs (YOLACT) instance segmentation model, which can avoid the interference of surroundings like cars and pedestrians. We acquired actual tram vehicle videos to validate our method which can accurately perceive the state and state transition continuously and real-timely in different complex scenes at any time, even if it stops and restarts within only 3 seconds. The approach gives a new thought and inspiration for studies of videos and illustrates that based on vehicle videos we can not only obtain the vehicle’s environment information but also perceive the self-vehicle state. And the proposed approach can be an alternative for estimating self-vehicle state when traditional methods are not available.

Design of reliable video image transmission system for underwater vehicle

In order to improve the transmission performance of underwater vehicle motion video image, a reliable transmission system of underwater vehicle motion video image is designed. The hardware part of the system includes the design of the transmission server and the design of the transmission client; the software part of the system mainly includes two parts: compression processing and lossless transmission of the motion video image of the underwater vehicle, which completes the software design of the system and realises the design of the whole system. The test results show that the proposed transmission system has good transmission performance for the motion video image of the vehicle in shallow water.

Application of Video Processing Technology Based on Diffusion Equation Model in Basketball Analysis

Video event detection and annotation work is an important content of video analysis and the basis of video content retrieval. Basketball is one of the most popular types of sports. Event detection and labeling of basketball videos can help viewers quickly locate events of interest and meet retrieval needs. This paper studies the application of anisotropic diffusion in video image smoothing, denoising, and enhancement. An improved form of anisotropic diffusion that can be used for video image enhancement is analyzed. This paper studies the anisotropic diffusion method for coherent speckle noise removal and proposes a video image denoising method that combines anisotropic diffusion and stationary wavelet transform. This paper proposes an anisotropic diffusion method based on visual characteristics, which adds a factor of video image detail while smoothing, and improves the visual effect of diffusion. This article discusses how to apply anisotropic diffusion methods and ideas to video image segmentation. We introduced the classic watershed segmentation algorithm and used forward-backward diffusion to process video images to reduce oversegmentation, introduced the active contour model and its improved GVF Snake, and analyzed the idea of how to use anisotropic diffusion and improve the GVF Snake model to get a new GGVF Snake model. In the study of basketball segmentation of close-up shots, we propose an improved Hough transform method based on a variable direction filter, which can effectively extract the center and radius of the basketball. The algorithm has good robustness to basketball partial occlusion and motion blur. In the basketball segmentation research of the perspective shot, the commonly used object segmentation method based on the change area detection is very sensitive to noise and requires the object not to move too fast. In order to correct the basketball segmentation deviation caused by the video noise and the fast basketball movement, we make corrections based on the peak characteristics of the edge gradient. At the same time, the internal and external energy calculation methods of the traditional active contour model are improved, and the judgment standard of the regional optimal solution and segmentation validity is further established. In the basketball tracking research, an improved block matching method is proposed. On the one hand, in order to overcome the influence of basketball’s own rotation, this article establishes a matching criterion that has nothing to do with the location of the area. On the other hand, this article improves the diamond motion search path based on the basketball’s motion correlation and center offset characteristics to reduce the number of searches and improve the tracking speed.

Mathematical Modeling and Simulation of Table Tennis Trajectory Based on Digital Video Image Processing

Digital sports training based on digital video image processing promises to reduce the reliance on the experience of coaches in the table tennis training process and to achieve a more general physical education base. Based on this approach, this paper describes the specific forms of exercise content, movement characteristics, and skill levels in the table tennis framework and specifies the calculation methods of motion capture and movement characteristics suitable for table tennis. Meanwhile, to further improve the accuracy of the inertial motion capture system in restoring the position posture of the trainees, this paper improves the original inertial motion capture system from two aspects: contact judgment of both feet and correction of the position posture based on the contact position constraint. The simulation results show that the corrected human posture has good action smoothness. This paper first proposes a knowledge-based generic sports-assisted training framework based on generalizing the traditional sports training model. The framework contains four main modules: domain knowledge, trainees, sport evaluation, and controller. The domain knowledge module is a digital representation of the knowledge of the exercise content, improvement instructions, and skill indicators of the sport; the trainee module is the active response of the trainee to the exercise content and improvement instructions; the motion evaluation module uses motion capture technology to obtain the raw motion data of the trainee and further calculates the motion characteristics; the controller module proposes improvement instructions to the trainee or makes him/her practice new content based on the results of the motion evaluation. Based on the results of the motion evaluation, the controller module proposes improvement instructions or makes the trainee practice new content until the trainee achieves the desired goal.

АЛГОРИТМ ПРЕДВАРИТЕЛЬНОЙ ОБРАБОТКИ ВИДЕОИЗОБРАЖЕНИЙ ДЛЯ ПОВЫШЕНИЯ ТОЧНОСТИ ОБНАРУЖЕНИЯ МАЛОРАЗМЕРНЫХ ОБРАЗОВ

Recognition of certain patterns in video images captured by a camera is carried out usingtraining methods based on convolutional neural networks. The larger the number of images withmultiple features and the more diverse the training sample of video images, the better the convolutionalneural networks extract features from the sequence of video images that were not included inthe training sample. This is a consequence of increasing the accuracy of detecting visual images onvideo images containing features of target images. However, there are limitations in improving thedetection performance when the size of the image to be detected is much smaller than the backgroundarea, or when the image is described with little information. To solve problems of this kind, the authorsof the article have developed an algorithm for the spatio-temporal integration of informationabout the movement of dynamic images. The algorithm processes a fixed number of video images atcertain points in time and extracts new independent signs of motion of dynamic images based onspace-time processing of video images. Further, it combines new local motion features with the originalvideo image features. This allows you to add a motion feature of dynamic images while preservingthe original image features that describe static images. Areas of the video image that characterizethe motion feature are displayed in a «color» cluster. The use of pre-processing is aimed at improving the accuracy of pattern detection, provided there are dynamic visual images on a static background.If the camera is in scan mode, a static background can be provided with a video stabilizer.Experimentally, estimates of integral criteria for the accuracy of detection neural network algorithmshave been obtained, showing an increase in the accuracy of detecting visual images usingthe algorithm for spatial-temporal integration of motion information.

Shapes and Rise Velocities of Single Bubbles in a Confined Annular Channel: Experiments and Numerical Simulations

Shapes and rise velocities of single air bubbles rising through stagnant water confined inside an annular channel were investigated by means of experiments and numerical simulations. Fast video imaging and image processing were used for the experiments, whilst the numerical simulations were carried out using the volume of fluid method and the open-source package OpenFOAM. The confinement of the annular channel did not affect the qualitative behavior of the bubbles, which exhibited a wobbling rise dynamic similar to that observed in bubbles rising through unconfined liquids. The effect of the confinement on the shape and rise velocity was evident; the bubbles were less deformed and rose slower in comparison with bubbles rising through unconfined liquids. The present data and numerical simulations, as well as the data collected from the literature for use here, indicate that the size, shape, and rise velocity of single bubbles are closely linked together, and prediction methods that fail to recognize this perform poorly. This study and the limited evidence documented in the literature indicate that the confinement effects observed in non-circular channels of complex shape are more complicated than those observed with circular tubes, and much less well understood.

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.

MWIR optoelectronic module based on the Russian 640512 InSb photodetector

Consideration is given to the results of the development of an optoelectronic module based on a Russian cooled matrix photodetector of 640512 elements format, operating
 in the spectral range of 3.6–4.9 microns, based on InSb. The paper describes the basic algorithms used for video image processing, describes the main blocks of the developed device, describes the methods for measuring NETD and spatial resolution, and gives the characteristics of the device.

Research on Highway Slope Disaster Automated Monitoring Method Based on Video Image Processing

With the rapid development of China highway transportation network, a lot of highways have risen in the mountainous areas. And the risk of geological disasters is also increasing. Due to the instability of the geological structure and geological changes, slope landslides and collapses frequently occur. However, there is a lack of effective means and tools in the field of highway slope disaster monitoring. For large and important slopes, various information-based monitoring methods are used, but the effects and practicability are not satisfactory. This paper proposes a highway slope disaster automated monitoring research method based on multi-camera video image processing to solve the previous engineering problems of slope video monitoring. It can quickly identify slope disaster events with centimeter-level monitoring accuracy, which can meet the application requirements of highway engineering. Correspondingly, the disaster emergency response capabilities of highway operators can also be supported and improved.

Image compression based on classification coding of constant-pitched functions transformers

The subject of research in the article are the processes of video image processing using an orthogonal transformation for data transmission in information and telecommunication networks. The aim is to build a method of compression of video images while maintaining the efficiency of its delivery at a given informative probability. That will allow to provide a gain in the time of delivery of compressed video images, a necessary level of availability and authenticity at transfer of video data with preservation of strictly statistical regulations and the controlled loss of quality. Task: to study the known algorithms for selective processing of static video at the stage of approximation and statistical coding of the data based on JPEG-platform. The methods used are algorithm based on JPEG-platform, methods of approximation by orthogonal transformation of information blocks, arithmetic coding. It is a solution of scientific task-developed methods for reducing the computational complexity of transformations (compression and decompression) of static video images in the equipment for processing visual information signals, which will increase the efficiency of information delivery.The following results were obtained. The method of video image compression with preservation of the efficiency of its delivery at the set informative probability is developed. That will allow to fulfill the set requirements at the preservation of structural-statistical economy, providing a gain in time to bring compressed images based on the developed method, relative to known methods, on average up to 2 times. This gain is because with a slight difference in the compression ratio of highly saturated images compared to the JPEG-2000 method, for the developed method, the processing time will be less by at least 34%.Moreover, with the increase in the volume of transmitted images and the data transmission speed in the communication channel - the gain in the time of delivery for the developed method will increase. Here, the loss of quality of the compressed/restored image does not exceed 2% by RMS, or not worse than 45 dB by PSNR. What is unnoticeable to the human eye.Conclusions. The scientific novelty of the obtained results is as follows: for the first time the method of classification (separate) coding (compression) of high-frequency and low-frequency components of Walsh transformants of video images is offered and investigated, which allows to consider their different dynamic range and statistical redundancy reduced using arithmetic coding. This method will allow to ensure the necessary level of availability and authenticity when transmitting video data, while maintaining strict statistical statistics.Note that the proposed method fulfills the set tasks to increase the efficiency of information delivery. Simultaneously, the method for reducing the time complexity of the conversion of highly saturated video images using their representation by the transformants of the discrete Walsh transformation was further developed. It is substantiated that the perspective direction of improvement of methods of image compression is the application of orthogonal transformations on the basis of integer piecewise-constant functions, and methods of integer arithmetic coding of values of transformant transformations.It is substantiated that the joint use of Walsh transformation and arithmetic coding, which reduces the time of compression and recovery of images; reduces additional statistical redundancy. To further increase the degree of compression, a classification coding of low-frequency and high-frequency components of Walsh transformants is developed. It is shown that an additional reduction in statistical redundancy in the arrays of low-frequency components of Walsh transformants is achieved due to their difference in representation. Recommendations for the parameters of the compression method for which the lowest value of the total time of information delivery is provided are substantiated.