Video Monitoring Technology Research Articles

A Wireless Self-Powered Sensor Network Based on Dual-Model Convolutional Neural Network Algorithm for Tennis Sports

More and more scholars turn their attention to the use of digital and systematic management methods to further improve the safety and effectiveness of tennis. At present, most of these systems are based on video monitoring technology, and their actual operation process is limited by the deployment environment, and the cost is very high. In order to obtain the track of tennis players, this paper uses the sensor array in the intelligent insole to collect the original data. A Dual-model Convolutional Neural Network (DMCNN) structure is designed to further improve the computational efficiency and accuracy of tennis players’ gait recognition. Combined with quaternion and complementary filtering algorithm, the rotation matrix is established to convert the data in the sensor coordinate system to the reference coordinate system. Use multiple sensors in the smart insole to collect linear and curve track data from tennis players. After the unit conversion and normalization of the original data, the acceleration and angular velocity data in the sensor coordinate system are converted to the reference coordinate system by using quaternions to represent the rotation matrix and using complementary filtering algorithm to update the quaternions. The experimental results show that the displacement error of the scheme is small and the performance is good when the track tracking of tennis players is realized. The error between the estimated distance and the actual distance is within 6%, which proves the accuracy of the linear trajectory experiment.

An Experience Sampling Analysis of the Impact of Video Monitoring Technology and In-Person Sitters on Nurse Burnout: The Moderating Effect of Nurse Commitment and Mediating Effect of Emotional Labor.

While it is established that video monitoring technology (compared with the use of in-person sitters) is a safe and cost-effective solution for hospitals, little is known about the impact of these approaches on nurses' stress and well-being. To compare the use of video monitoring technology and in-person sitters (likely a resource reallocated from nurses) for monitoring patients on nurses' emotional labor and burnout. An experience sampling method was conducted by surveying nurses twice a day for 3 weeks, resulting in 524 survey administrations provided by 74 nurses. The surveys included measures of daily video monitoring technology and in-person sitter use, emotional labor, emotional exhaustion, and nurse career commitment. There were positive effects from video monitoring technology and negative effects of in-person sitters on emotional labor and emotional exhaustion, particularly for nurses lower in commitment. Hospital adoption of video monitoring technology has a positive impact on nurses compared with in-person sitter use.

Application of SSD network algorithm in panoramic video image vehicle detection system

Abstract Due to the popularity of high-performance cameras and the development of computer video pattern recognition technology, intelligent video monitoring technology is widely used in all aspects of social life. It mainly includes the following: industrial control system uses video monitoring technology for remote monitoring and comprehensive monitoring; in addition, intelligent video monitoring technology is also widely used in the agricultural field, for example, farm administrators can view the activities of animals in real time through smart phones, and agricultural experts can predict future weather changes according to the growth of crops. In the implementation of intelligent monitoring system, automatic detection of vehicles in images is an important topic. The construction of China’s Intelligent Transportation System started late, especially in video traffic detection. Although there are many related studies on video traffic detection algorithms, these algorithms usually only analyze and process information from a single sensor. This article describes the application of the single-shot detector (SSD) network algorithm in a panoramic video image vehicle detection system. The purpose of this article is to investigate the effectiveness of the SSD network algorithm in a panoramic video image vehicle detection system. The experimental results show that the detection accuracy of a single convolutional neural network (CNN) algorithm is only 0.7554, the recall rate is 0.9052, and the comprehensive detection accuracy is 0.8235. The detection accuracy of SSD network algorithm is 0.8720, recall rate is 0.9397, and the comprehensive detection accuracy is 0.9046, which is higher than that of single CNN algorithm. Thus, the proposed SSD network algorithm is compared with a single convolution network algorithm. It is more suitable for vehicle detection, and it plays an important role in panoramic video image vehicle detection.

Robust Home Alone Security System Using PIR Sensor and Face Recognition

CCTV-based video monitoring technology is one of the fastest growing security technologies markets. The existing video monitoring systems are, however, still not in a position to be used to prevent crime. For public safety purposes, large networks of cameras are increasingly deployed in public places like Residential Buildings, College Campus, offices, airports, railway stations, and shopping malls. Such systems are primarily dependent on human observers and are therefore limited over long periods by factors such as exhaustion and monitoring. In order to overcome this constraint, "intelligent" systems are required, which can highlight the critical data and remove normal conditions that are not a safety hazard. We propose a model utilizing machine learning techniques in order to build these smart systems. This research aims to create an application in real time, which is necessary for labs, places of work or homes where human detection and Recognition will be done for human safety

Real-Time Analysis of Athletes’ Physical Condition in Training Based on Video Monitoring Technology of Optical Flow Equation

This article is dedicated to the research of video motion segmentation algorithms based on optical flow equations. First, some mainstream segmentation algorithms are studied, and on this basis, a segmentation algorithm for spectral clustering analysis of athletes’ physical condition in training is proposed. After that, through the analysis of the existing methods, compared with some algorithms that only process a single frame in the video, this article analyzes the continuous multiple frames in the video and extracts the continuous multiple frames of the sampling points through the Lucas-Kanade optical flow method. We densely sampled feature points contain as much motion information as possible in the video and then express this motion information through trajectory description and finally achieve segmentation of moving targets through clustering of motion trajectories. At the same time, the basic concepts of image segmentation and video motion target segmentation are described, and the division standards of different video motion segmentation algorithms and their respective advantages and disadvantages are analyzed. The experiment determines the initial template by comparing the gray-scale variance of the image, uses the characteristic optical flow to estimate the search area of the initial template in the next frame, reduces the matching time, judges the template similarity according to the Hausdorff distance, and uses the adaptive weighted template update method for the templates with large deviations. The simulation results show that the algorithm can achieve long-term stable tracking of moving targets in the mine, and it can also achieve continuous tracking of partially occluded moving targets.

Computer Big Data Technology in Internet Network Communication Video Monitoring of Coal Preparation Plant

The paper uses the control technology of computer big data and PLC, fieldbus communication technology and video monitoring technology to research, design and develop the monitoring system of the coal preparation plant’s production process. In this plan, the coal preparation plant’s video monitoring system, production centralized control system and other production support systems are fully integrated through network technology, to achieve the purpose of improving its safety guarantee function. The system improves the level of visual management, realizes unattended operation, and reduces potential safety hazards.

基于人工神经网络算法的农业机械视频监控体系优化研究

Aiming at the demand of mileage statistics, work area statistics, fault site return and related data automatic retention in the current agricultural machinery reliability appraisal process, the optimization of agricultural machinery video monitoring system based on artificial neural network algorithm was studied. Together with the new video monitoring technology, the agricultural machinery GPS, GSM and fuel consumption recorder technology are combined to realize the functions of real-time data transmission, monitoring, analysis and statistics. Aiming at intelligent fault analysis, a real-time online detection mechanism is proposed, and a cloud collaborative detection mechanism is proposed to solve the problem of inaccurate offline model detection. Use plane map or satellite map to browse. Thus, an online monitoring and visual testing platform for agricultural machinery faults without real-time monitoring records is established. Finally, the test platform is tested and applied. Test results show that the algorithm can greatly shorten the training time and improve the accuracy of training model detection. With the increase of online training iterations, it is helpful to improve the detection accuracy of the generated model. In a word, the system service platform can provide scientific and transparent data for agricultural machinery fault identification, ensure the scientific, open and fair principles of agricultural machinery fault identification, and greatly improve the efficiency of agricultural machinery management.

BSVMS: Novel Autonomous Trustworthy Scheme for Video Monitoring

With the continuous development and application of monitoring technology, which involves increasingly more sensitive information, the global demand for video monitoring systems has surged. As a result, video monitoring technology has received widespread attention both at home and abroad. Traditional video monitoring systems experience security threats, with differing levels of severity, in terms of attack, storage, transmission, etc., which results in different degrees of damage to users’ rights. Therefore, we propose a blockchain‐SM‐based video monitoring system called BSVMS. For the front‐end device invasion risk, internal attack risk, and security storage problem of the monitoring system, we use commercial cryptography algorithms to complete the encryption processing of images through a visual change network in the imaging process, thereby ensuring the security of the video data from the source. To address the problem that the video monitoring application software and data are vulnerable to damage, we use blockchain technologies that are tamper‐proof and traceable to build a trustworthy video monitoring system. In the system, no member can query the original monitoring data. To address the security issues in network transmission, we use a commercial cryptography algorithm for multilayer encryption to ensure the security of data during transmission, guarantee the confidentiality of the system, and realize domestic autonomous control. We then conduct tests and security analysis of the encryption and decryption efficiency of the SM4 algorithm used in the system, the blockchain performance, and the overall performance. The experimental results show that in this system environment, the SM4 algorithm encryption and decryption efficiency is better than other algorithms and that the blockchain used meets industry standards.

Intelligent Video Surveillance Technology in Intelligent Transportation

Along with the strength of the country’s overall strength, the people’s pockets have become more and more popular, and there have been significant improvements in all aspects of life, especially in terms of travel methods. This reflects the increase in residents’ income, but it also brings huge traffic pressure. In the long run, traffic congestion is not only detrimental to urban development, but frequent traffic accidents threaten residents’ travel safety. Effective monitoring methods are essential to solving these problems, so it is necessary to carry out research on intelligent video monitoring technology in intelligent transportation. The purpose of this article is to solve the current situation of excessive traffic pressure in the city. Through the study of intelligent video surveillance technology in intelligent traffic, the use of constrained least squares algorithm to remove motion blur and apply Kalan filtering to the sharpening process is used to eliminate noise ambiguity and make a brief introduction to various classic moving target detection methods to realize real-time monitoring of intelligent traffic conditions and continuously adjust and verify the monitoring situation, and then establish intelligent video in intelligent traffic monitoring technology research system. The research results show that this kind of intelligent video surveillance technology research in intelligent transportation can effectively increase the awareness of intelligent video surveillance technology and improve the level of intelligent video surveillance technology. The data measurement time has been shortened by one hour, the aggregation time has been changed from three hours to two hours, and the analysis time has been shortened by half. Eased urban traffic road pressure and greatly reduced the incidence of traffic accidents, which is conducive to socialist harmony social construction.

Research on Video Detection of Object Intrusion in Substation

The safe running of substations is an important part of power system security, as well as an important work of the power companies. The intrusion of kites, plastic trash, small animals and other objects will affect the safe running of substations. With the development of video monitoring technology, intelligent recognition technology of images and videos, video monitoring has gradually become the main monitoring means of unattended substations. This paper firstly analyses the research status and main technologies of video object recognition. To achieve the detection of foreign objects in substations, moving object detection and video object detection technologies are studied. And two outlines are proposed: using background subtraction and image classification, using YOLOv3 to detect objects in videos. In the end of the paper, YOLOv3 was tested by training a detector of small animals. The results show that the value of mAP equals to 0.7169 and the detection speed fulfills the request of real-time detection. This method could serve as reference for detecting and tracking intrusion objects in substations.

Technology Utilization in Fall Prevention.

Falls, defined as unplanned descents to the floor with or without injury to an individual, remain to be one of the most challenging health conditions. Fall rate is a key quality metric of acute care hospitals, rehabilitation settings, and long-term care facilities. Fall prevention policies with proper implementation have been the focus of surveys by regulatory bodies, including The Joint Commission and the Centers for Medicare and Medicaid Services, for all healthcare settings. Since October 2008, the Centers for Medicare and Medicaid Services has stopped reimbursing hospitals for the costs related to patient falls, shifting the accountability for fall prevention to the healthcare providers. Research shows that almost one-third of falls can be prevented and extensive fall prevention interventions exist. Recently, technology-based applications have been introduced in healthcare to obtain superior patient care outcomes and experience via efficiency, access, and reliability. Several areas in fall prevention deploy technology, including predictive and prescriptive analytics using big data, video monitoring and alarm technology, wearable sensors, exergame and virtual reality, robotics in home environment assessment, and personal coaching. This review discusses an overview of these technology-based applications in various settings, focusing on the outcomes of fall reductions, cost, and other benefits.

Design and Research of Ultrasonic Diagnostics Online Teaching Based on Blackboard

With the continuous development of medical ultrasound diagnostics technology, ultrasound in obstetrics and gynecology has become an essential branch of ultrasound medical diagnostics, and increasing attention has been paid to the teaching of ultrasound in obstetrics and gynecology diagnostics. Based on the modern blackboard network platform and mobile video monitoring technology, a vivid and intuitive online course of ultrasound diagnostics in obstetrics and gynecology is established, which is conducive to implementing communications and interactions among students, teachers, and resources, improving the teaching quality and learning efficiency, and cultivating ultrasound diagnostics skills of medical students.

The Application of A-CNN in Crowd Counting of Scenic Spots

In places where people are concentrated, such as scenic spots, the statistical accuracy of existing crowd statistics algorithms is not enough. In order to solve this problem, a crowd counting algorithm based on adaptive convolution neural network (A-CNN) is proposed, which is based on video monitoring technology. The process of its pooling is dynamically adjusted according to different feature graphs. Then the pooled weights are adjusted adaptively according to the contents of each pooled domain. Therefore, CNN can extract more accurate features when processing different pooled domains under different iteration times, so as to achieve adaptive effect finally. The experimental results show that the proposed A-CNN algorithm has improved the recognition accuracy.

Pre-Lung Transplant Waitlist Mortality Rate Reduction Using Remote Video Monitoring Technology

Purpose Lung transplant (LTx) candidates often have multiple medical comorbidities along with end stage lung disease. As their lung disease progresses, they experience increased symptom burden and increasing hypoxia and oxygen requirements, increasing the risk of delirium. This poses an increased safety risk, especially if they become disconnected from their supplemental oxygen supply. The use of remote video monitoring (RVM) has been shown to improve safety and prevent adverse events in various hospitalized patient populations. However, it has not been previously evaluated for the pre-LTx patient population. Our objective was to investigate the feasibility of RVM in this population. Methods We conducted a feasibility pilot from 07-2016 to 12-2017. Rapidly deteriorating pre-LTx patients that were admitted to hospital and requiring high flow oxygen support were observed with RVM. Consent was obtained from all patients and/or family. Mortality rate and adverse event incident reports were assessed prior to and post implementation of RVM. A satisfaction survey was also administered to patients, family and healthcare providers. Results Fifty-three consecutive patients admitted to hospital while on the LTx waitlist were included in the pilot. The average length of time monitored was 136 hours (range 18 - 715). The mortality rate of LTx candidates was reduced from 21% to 6% post implementation of RVM. Reporting of adverse events causing severe patient harm was also reduced. Overall, patients, families and healthcare providers provided positive feedback on the reassurance and overall performance provided of the RVM program. Conclusion Our findings suggest that RVM in hospitalized end stage lung disease patients listed for LTx is feasible and enhances patient safety and satisfaction.

Improving Patient Safety Through Video Monitoring

Abstract Purpose: Falls are a major safety issue in rehabilitation settings. Patients receive mixed messages—try to be as independent as possible, but don’t do anything in your room without calling for assistance. Despite the use of multiple falls interventions at this facility, the fall rate remained high. To impact this rate, the facility implemented a video monitoring system. This system allows for patients at risk for falling to be monitored from a remote location. The monitor technician is able to speak to the patient directly and/or contact staff members to respond to the room, preventing a fall. Design: Sequential cohort design. Method: Fifteen video monitoring units were installed on high-risk units in a 115-bed inpatient rehabilitation facility. Total falls and falls rates were tracked and reported pre- and postimplementation. Findings: Over a 21-month period prior to implementing the video monitoring system, the average hospital-wide rate of falls was 6.34 per 1,000 patient-days (SD = 1.7488). After a year of usage, that average has decreased to 5.099 falls per 1,000 patient-days (SD = 1.524). The reduction in falls was statistically significant. In addition, there have been significant cost savings by reducing sitter usage. Conclusions: Video monitoring can improve patient safety by decreasing falls; decreasing sitter usage and cost; and improving patient, family, and staff satisfaction. Clinical Relevance: Falls are a significant issue in rehabilitation settings, and current fall prevention strategies fall short of reducing fall rates. Implementation of new video monitoring technology can help reduce fall rates in inpatient rehabilitation settings.

Application of Information Technology in Comprehensive Reform of Agricultural Water Price in Irrigation District

This paper has carried out the research on the application of information technology in the comprehensive reform of agricultural water price of irrigation district. Firstly, we have analyzed of the demand system, put forward the structure scheme and information system. Secondly, we have researched on the flow measurement of open channel technology, automatic gate lifting control technology, video monitoring technology, automatic irrigation and soil moisture monitoring control technology based on RF IC card water fee management technology. The software development environment was Visual Studio2015, and the structured methods are adapted to designing databases. Finally, we have completed the system management software design, RF IC card control software, it can be suitable for the comprehensive reform of agricultural water price in large and medium-sized irrigation areas, effectively enhancing the intelligent level of irrigation water management in irrigation area.

Research on Logistics Solutions for Dangerous Chemical Products

The objective of this paper is to design and realize the logistic monitoring system for dangerous chemical products. As for problems of robbery accidents and leakage accidents during the transportation process of dangerous chemical products, the identification and tracking technology on moving objects in the real-time video stream transmission and video sequence are the main research objects; and the specific discussion and research on the video monitoring technology in the logistic monitor system of dangerous chemical products is carried out based on the research on theory principle and technology realization of existing video technology. The real-time transmission of video stream is achieved by collecting the video of YUV4:2:2 form, video coding & compression of H264 form and using RTP protocol, so that the identification and tracking on moving objects in the video sequence can be achieved. The experiment result indicates that the logistic monitoring system for dangerous chemical products designed in this paper can be effectively applied to identify the moving objects. Therefore, the logistic monitoring system for dangerous chemical products can be used to improve the stability and effectiveness of the transportation of dangerous chemical products.

Research on Embedded Network Real-Time Video Monitoring System Based on Zynq

With the improvement of the performance of the embedded processor, the real-time network video monitoring system based on embedded technology becomes a developing direction of the network video with its low price and portability. In this paper, software and hardware co-design method is adopted to design an embedded system. ARM of the Zynq chip is responsible for embedded system structures and high definition video processing, and FPGA is used to design other logic and hardware expansion. Compared with the traditional network video monitoring technology, the embedded network video monitoring technology based on Zynq not only improves the quality of the image, but also has better real-time performance and scalability.

Remote Video Monitoring: A Novel Approach in Fall Prevention.

Adequate fall prevention interventions are a challenge that nurses continue to endure. Remote video monitoring can be used in conjunction with other fall prevention interventions. This article describes remote video monitoring technology and the benefits and challenges associated with its implementation. J Contin Educ Nurs. 2016;47(11):484-486.

Improving Patient Safety Through Video Monitoring.

Falls are a major safety issue in rehabilitation settings. Patients receive mixed messages-try to be as independent as possible, but don't do anything in your room without calling for assistance. Despite the use of multiple falls interventions at this facility, the fall rate remained high. To impact this rate, the facility implemented a video monitoring system. This system allows for patients at risk for falling to be monitored from a remote location. The monitor technician is able to speak to the patient directly and/or contact staff members to respond to the room, preventing a fall. Sequential cohort design. Fifteen video monitoring units were installed on high-risk units in a 115-bed inpatient rehabilitation facility. Total falls and falls rates were tracked and reported pre- and postimplementation. Over a 21-month period prior to implementing the video monitoring system, the average hospital-wide rate of falls was 6.34 per 1,000 patient-days (SD = 1.7488). After a year of usage, that average has decreased to 5.099 falls per 1,000 patient-days (SD = 1.524). The reduction in falls was statistically significant. In addition, there have been significant cost savings by reducing sitter usage. Video monitoring can improve patient safety by decreasing falls; decreasing sitter usage and cost; and improving patient, family, and staff satisfaction. Falls are a significant issue in rehabilitation settings, and current fall prevention strategies fall short of reducing fall rates. Implementation of new video monitoring technology can help reduce fall rates in inpatient rehabilitation settings.