Video Analysis Technology Research Articles

Perioperative Nursing Care of Vascular Decompression for Trigeminal Neuralgia under AR Medical Technology.

AR technology, also known as AR or virtual reality, refers to a technology that combines and allows interaction of the virtual world on the display system with the real world through the position and angle of the camera video and image analysis technology. This technology is different from VR technology, and its characteristics can be easily explained as follows: when using AR technology, the user's eyes can see not only the real world but also the virtual world derived from the computer through things in the real world. At present, AR has been widely used in education, engineering, entertainment, and medical fields. In order to provide better perioperative care and bring patients a good nursing experience, this article mainly introduces the perioperative care of vascular decompression in the treatment of trigeminal neuralgia by augmented reality medical technology, in order to provide better care for patients with trigeminal neuralgia. This article proposes the perioperative nursing research method of vascular decompression for the treatment of trigeminal neuralgia under AR medical technology, including an overview of trigeminal neuralgia, perioperative related research, and AR medical technology algorithms, and designs related experiments to study whether AR medical technology can bring good news to nursing. Experimental results show that 96% of patients believe that with the enhancement of realistic medical technology, perioperative vascular decompression care for trigeminal neuralgia can help them recover faster and can be gradually popularized.

Using video-analysis technology to estimate social mixing and simulate influenza transmission at a mass gathering

Mass gatherings create settings conducive to infectious disease transmission. Empirical data to model infectious disease transmission at mass gatherings are limited. Video-analysis technology could be used to generate data on social mixing patterns needed for simulating influenza transmission at mass gatherings. We analyzed short video recordings of persons attending the GameFest event at a university in Troy, New York, in April 2013 to demonstrate the feasibility of this approach. Attendees were identified and tracked during three randomly selected time periods using an object-tracking algorithm. Tracks were analyzed to calculate the number and duration of unique pairwise contacts. A contact occurred each time two attendees were within 2 m of each other. We built and tested an agent-based stochastic influenza simulation model assuming two scenarios of mixing patterns in a geospatially accurate representation of the event venue —one calibrated to the mean cumulative contact duration estimated from GameFest video recordings and the other using a uniform mixing pattern. We compared one-hour attack rates (i.e., becoming infected) generated from these two scenarios following the introduction of a single infectious seed. Across the video recordings, 278 attendees were identified and tracked, resulting in 1,247 unique pairwise contacts with a cumulative mean contact duration of 74.76 s (SD: 80.71). The one-hour simulated mean attack rates were 2.17 % (95 % CI:1.45 – 2.82) and 0.21 % (95 % CI: 0.14 – 0.28) in the calibrated and uniform mixing model scenarios, respectively. We simulated influenza transmission at the GameFest event using social mixing data objectively captured through video-analysis technology. Microlevel geospatially accurate simulations can be used to assess the layout of event venues on social mixing and disease transmission. Future work can expand on this demonstration project to larger spatial and temporal scenes in more diverse settings.

Research on Workflow Task Scheduling Strategy in Edge Computer Environment

In recent years, with the rapid development of science and technology, edge technology has been gradually applied to many fields, such as computer cloud environment. Through edge computing, we can schedule cloud workflow tasks, which can allocate the workflow tasks submitted by users to appropriate computing resources. Then, based on the amount of resources used, we can pay for it immediately. For most ordinary users, workflow task scheduling is to balance the completion time and execution cost reasonably, which can coordinate the two into the optimal. With the continuous development of science and technology, intelligent mobile terminal equipment has gradually replaced the electronic computer, which has become the main computing platform for consumers. At the same time, with the development of natural language processing and video analysis technology, the world’s mobile data traffic has become an explosive growth. “Mobile edge computing” is a way for mobile network edge to provide IT service environment and cloud computing capability, which will reduce the delay of network operation and service delivery. By emphasizing proximity to mobile users, we can improve the user experience. Firstly, this paper analyzes the related concepts. Then, this paper mainly studies the workflow task scheduling strategy.

Research on deep learning method for rail surface defect detection

Rail surface defect detection plays a critical role in the maintenance of the rail transportation system. Video analysis technology is a promising method to detect defects due to its low cost and effectiveness. Several attempts with hand-craft features have been made to obtain the detection results by using traditional machine vision algorithms. However, these methods suffer from imprecise results due to challenging conditions, such as deteriorated and changeable lighting environment and various types of complex rail surface defects. Recently, classification methods with complex deep convolutional networks have become popular. Despite their high accuracy, these methods cannot meet the requirements of defects localisation and real-time processing in practice. To solve these problems, this study proposes a novel object detection algorithm to detect rail defects. The net architecture of the proposed algorithm includes a backbone network using MobileNet and several novel detection layers with multi-scale feature maps inspired by you only look once (YOLO) and feature pyramid networks. Two different architectures of MobileNet are used to estimate the performance of defects detection. The experimental results demonstrate the great potential of the proposed algorithm with fast inference speed and high accuracy in the industry.

Application of computer vision technologies for automatic data collection about emotions of students during group work

Today the global scientific community is actively discussing the issues on the application of artificial intelligence in education. One of the least studied technologies in terms of its application in education is computer vision. The development and implementation of intelligent systems based on video analysis and machine learning algorithms provide new opportunities for teachers and staff of the educational organization administration to understand and transform the educational process. The article discusses the use of video analysis technologies from cameras with a 360-degree view to collect data on the emotional state of students during group work in the classroom. In the course of the described research, a software solution for automatic emotions data collection during students’ teamwork leaning was developed. This solution can be used for future research aimed at studying the impact of emotional state on students’ educational success. The results of the study can be used to form the research agenda of Russian universities in order to implement the objectives of the section “Education and personnel” of the program “Digital economy of the Russian Federation”, approved by the Government of the Russian Federation.

Relationship Between Sleep Quantity And Quality And Performance Variables In Female Collegiate Soccer Players.

Support for sport-specific benefits of sleep quantity and quality are limited, particularly in female collegiate athletes. PURPOSE: To evaluate the relationship between sleep quantity and quality and soccer-specific performance variables in an elite group of female soccer players. METHODS: Eight NCAA college-aged competitive Division I women’s soccer players (18-23 yrs) participated in the study. Global Positioning Systems (GPS), heart rate monitoring, and video analysis technologies were used during four matches along with 24-hour actigraphy; actigraphy was also used to measure sleep quantity and quality. The night prior to the match was used for data analyses of sleep quantity and quality. A multivariate analysis of variance (MANOVA) was utilized to determine whether the vectors of the means in groups of variables were significant. Paired t-tests were used to analyze if differences in variables of performance existed after the “best” and “worst” nights of sleep for quantity and quality. Significance was set at p < 0.05. RESULTS: Mean sleep quantity and quality for the “best” (575.0 ± 38.4 minutes, 91.9 ± 2.6% of time in bed spent sleeping) and “worst” (416 ± 57.6 minutes, 76.2 ± 12.1% of time in bed spent sleeping) nights were significantly different. The MANOVAs were not significantly different for the physiological and physical variables for quantity and quality of sleep. No differences in performance variables were observed after “best” and “worst” quantity sleep matches. However, percent time spent exercising above 85% of the maximal heart rate (HRmax) was significantly lower after the “best” quality night of sleep (30.2 ± 13.5 vs. 47.9 ± 24.3%), even though overall heart rate exertion was higher after the “best” night of sleep (518.5 ± 193.1 vs. 387.6 ± 148.9 AU). High metabolic load distance (distance running at speeds greater than 19 km/hr) and distance accelerating or decelerating quickly (>2 m/s2) was lower (11.8 ± 3.6 vs. 16.3 ± 6.0 yds/min) and the number of decelerations was higher after the “best” quality night (69.7 ± 28.1 vs. 50.6 ± 25.9). CONCLUSION: While quantity of sleep did not influence performance related outcomes, quality of sleep may be important for reducing the time spent exercising > 85% HRmax and reducing incidence of high orthopedic stresses.

Blockchain based privacy preserving multimedia intelligent video surveillance using secure Merkle tree

The recent intelligent surveillance systems provide various services that have not existed before, such as proactive predictive surveillance, through the AI-based video analysis technology. However, there are common security and privacy challenges caused by malicious attacker and untrusted 3rd party in the CCTV cloud system. To solve this problem, we exploit the blockchain technology to insure integrity and security of cloud-based intelligent surveillance system. In this paper, we propose a new method for cloud-based CCTV blockchain system. Our contribution is to propose a new Merkle-Tree method for the efficient transmission of video data. The suggested method in this paper is efficient because it reduces the bandwidth required for transmission. It also has the advantage of enabling deduplication to reduce storage costs. Also, the proposed method can synchronize CCTV video data safely without exposing the privacy of the object in the course of synchronization.

Research on video enhancement concentration and intelligent monitoring technology in Substation

With the development of intelligent theory of computer vision and the popularization of surveillance cameras, intelligent video analysis and surveillance technology has been widely used. The traditional video monitoring system will produce a large amount of video information in the application process, which is not conducive to the storage and reference of information, and it can not carry out real-time warning, and has poor robustness to the bad environment. In view of these disadvantages, this paper proposes an improved video enhancement and concentration method, which has a good effect in ensuring the video concentration ratio and the target loss rate, and can improve the shortcomings brought by manual reference. Aiming at the problem of real-time early warning, an intelligent video analysis method is proposed to realize the functions of event alarm and detection analysis. It is of great significance to improve the video monitoring management level of the substation and realize the unattended operation of the substation.

Human Emotion Detection using Machine Learning Techniques

Image processing is a method to convert an image into digital form and perform some operations on it. This is done to enhance the image or to extract useful information from it. Facial expressions are nonverbal form of communication. There are eight universal facial expressions which include: neutral, happy, sadness, anger, contempt, disgust, fear, and surprise. So it is very important to detect these emotions on the face. A monitoring system for elderly people which is based on technology involving recognizing emotions from video image. Our proposed system includes video analysis technology which includes the data from video is adopted to realize monitoring elders' living conditions in real time. In case of emergency, the system will alert their relatives and children by sending a message.

Video Analysis Technology and Its Application in Badminton Sports Training

Based on the continuous development of computer vision and graphics technology, pattern recognition and image processing technology, intelligent video analysis technology combines these related technologies to establish a mapping relationship between image description and surveillance image. Intelligent video analysis technology has a wide range of applications, such as in military and economic fields. In the process of badminton training, when the trainer explains the action essentials for the athletes, the video replay and analysis management methods are less difficult, lack of science and intuition and it is difficult to meet the trainer’s demand for sports performance evaluation. The analysis and recognition process of badminton video are studied. Based on particle filter prediction, adaptive threshold algorithm is used to achieve adaptive separation of moving targets. The particle filter technology is used to track the trajectory of the athletes and the motion model is constructed. Based on this, the position of the important joint points in the subsequent motion video frames is predicted, so as to track the subsequent motion video frames. When the motion recognition in the badminton training video is used, the conditional random field method is mainly used, which makes the motion recognition rate higher and the false separation rate lower.

RETRACTED ARTICLE: Application research of personnel attendance technology based on multimedia video data processing

At present, the access control system-based face recognition mostly use devices similar to fingerprint punchers for face collection and recognition. Its recognition accuracy is low, and it leads to frequent problems in personnel attendance. Based on this, this study combines multimedia image and video analysis technology to adopt multi-angle video collection for personnel attendance. At the same time, this study uses Shumate’s method for video preliminary processing, and uses histogram equalization processing to perform deep processing of the image. In addition, through the closed operation, the discrete points in the skin color region can be connected with the skin color region, so that the face region is more fully enriched, and the system is constructed on the basis of the technology. In our work, the average recognition rate has reached 94.40% when the number of samples is 5. Through comparative analysis, the algorithm of this study has certain practicality, which can provide theoretical reference for subsequent related research.

Automatic detection of ruminant cows’ mouth area during rumination based on machine vision and video analysis technology

Automatic detection of ruminant cows’ mouth area during rumination based on machine vision and video analysis technology

A real time aggressive human behaviour detection system in cage environment across multiple cameras

The monitoring of activities in the enclosed cage environments to detect abnormalities such as aggressive behaviour, employing a real-time video analysis technology, has become an emerging and challenging problem. Such system should be able: 1) to track individuals; 2) to identify their action; 3) to keep a record of how often the aggressive behaviour happened, at the scene. On top of that, the system should be implemented in real-time, whereby, the following limitations should be taken into consideration: 1) viewing angle (fish-eye); 2) low resolution; 3) number of people; 4) low lighting (normal); 5) number of cameras. This paper proposes to develop a vision-based system that is able to monitor aggressive activities of individuals in an enclosed cage environment using multiple cameras considering the above-mentioned conditions. Experimental results show that the proposed system is easily realised and achieved impressive real-time performance, even on low end computers.

EXTRACTING 3D SEMANTIC INFORMATION FROM VIDEO SURVEILLANCE SYSTEM USING DEEP LEARNING

Abstract. At present, intelligent video analysis technology has been widely used in various fields. Object tracking is one of the important part of intelligent video surveillance, but the traditional target tracking technology based on the pixel coordinate system in images still exists some unavoidable problems. Target tracking based on pixel can’t reflect the real position information of targets, and it is difficult to track objects across scenes. Based on the analysis of Zhengyou Zhang's camera calibration method, this paper presents a method of target tracking based on the target's space coordinate system after converting the 2-D coordinate of the target into 3-D coordinate. It can be seen from the experimental results: Our method can restore the real position change information of targets well, and can also accurately get the trajectory of the target in space.

Intelligent monitoring method of cow ruminant behavior based on video analysis technology

To overcome the limitations of traditional dairy cow's rumination detection methods, a video-based analysis on the intelligent monitoring method of cow ruminant behavior was proposed in this study. The Mean Shift algorithm was used to track the jaw motion of dairy cows accurately. The centroid trajectory curve of the cow mouth motion was subsequently extracted from the video. In this way, the monitoring of the ruminant behavior of dairy cows was realized. To verify the accuracy of the method, six videos, a total of 99'00, 24 000 frames were selected. The test results demonstrated that the success rate of this method was 92.03%, despite the interference of behaviors, such as raising or turning of the cow’s head. The results demonstrate that this method, which monitors the ruminant behavior of dairy cows, is effective and feasible. Keywords: dairy cow, rumination, intelligent monitoring, video analysis, animal bahavior DOI: 10.25165/j.ijabe.20171005.3117 Citation: Chen Y J, He D J, Fu Y X, Song H B. Intelligent monitoring method of cow ruminant behavior based on video analysis technology. Int J Agric & Biol Eng, 2017; 10(5): 194–202.

The Information and Communications Technology in Higher Education: A YouTube Channel as a Resource

The information technologies and communication (ICT) are part of methodological change in university teaching within the framework of the European Higher Education Area. One of the most used ICT within observational of fitness activity-sports are the video analysis techniques. In this regard, free software and free access program on line are sustainable tool for teaching university. This research to implementing the video analysis technology in physical education for primary education classes, with the objective of improve teaching intervention processes. The creation of a YouTube private cannel, the use of video editors and video download on line, or the use of apps for mobile devices in processes evaluation, they are elements to complete the research. 84.3% of students said that the experience has been useful. 41.2% did not know edit videos and after the experience itself. 85.3% prefer not to perform this kind of task instead written work.

Characterization of drill bit breakage in PCB drilling process based on high-speed video analysis

PurposeThe purposes of this paper are to study the characterization of drill bit breakage in printed circuit board (PCB) drilling process based on high-speed video analysis and to provide an important reference for micro drill bit breakage prediction.Design/methodology/approachBased on PCB drilling experiment, the high-speed camera was used to observe the micro drill breakage process and the chip removal process. The variation of chip in the drilling process was studied and one of the key reasons for the drill bit breakage was analysed. Finally, the swing angles’ feature during the breakage process of the micro drill was analysed and researched with the image processing tools of MATLAB.FindingsThe micro drill was prone to breakage mainly because of the blocked chips. The breakage process of the micro drill can be divided into the stage of stable chips evacuation, the stage of blocked chips and the stage of drill bit breakage. The radians of swing angles were basically in the range of ±0.01 when the drilling possess is normal. But when the radians of swing angles considerably exceeded the range of ±0.01, the micro drill bit may be fractured.Originality/valueThis paper presented the method to study the characterization of drill bit breakage in the PCB drilling process by using high-speed video analysis technology. Meanwhile, an effective suggestion about monitoring the radians of swing angles to predict the breakage of micro drill bit was also provided.

Intelligent monitoring method of cow ruminant behavior based on video analysis technology

Intelligent monitoring method of cow ruminant behavior based on video analysis technology

Research on Video Analysis and Tracking Technology in Swimming Video

Research on Video Analysis and Tracking Technology in Swimming Video

A Method to Evaluate Vehicle Impact Speeds in Passenger Car-Pedestrian Accidents

This paper aimed to research the relationship between the wrap around distance (WAD) to head contact and vehicle impact speed based on real pedestrian traffic accidents with video. A team was established to collect passenger car-pedestrian accident cases occurring between July 2011 and July 2015 in Chongqing, China. A total of 15 pedestrian crashes were selected into the sample. Impact speeds were calculated by a video analysis technology, and the WAD was revised according to the average height of pedestrians involved in the sample. The relationship between the WAD and impact speed was analyzed using linear regression analysis. We propose a method to evaluate the impact speed in passenger car-pedestrian. These results will contribute to the development of judicial identification and research of pedestrian injury.