Video Monitoring System Research Articles

Agent-based modeling system for real-time characterization of film thickness in digital twin coating

In the automated spraying workshop of ship segments, poor visibility caused by dust and fog hampers the effectiveness of the video monitoring system. The traditional method used during the automated spraying process fails to provide real-time measurement of film thickness and prediction of spraying quality, resulting in uneven film thickness and high rework rates. Robotic spraying technology also faces challenges, such as the time-consuming and labor-intensive process of building and computing a highly reliable Computational Fluid Dynamics (CFD) based spraying film-forming simulation model, which cannot be reflected in real-time during the spraying process. The digital twin characterization method, based on the agent model, offers a solution to these limitations and challenges, providing new ideas and approaches for improving and optimizing the spray film formation model. This paper presents the development of a real-time characterization system for sprayed film thickness based on digital twin technology. The spraying film thickness under different working conditions is obtained using Ansys-Fluent simulation software by varying the gun height and gun speed. The predictive model for spraying film thickness is then established using the obtained simulation data and the corresponding gun height and gun speed, employing the surrogate modeling method (BP neural network). The real-time characterization system for spraying film thickness is developed by combining C# and Unity3D. Finally, in order to ensure the accuracy of the predictive model, a spraying experimental platform is built to verify the accuracy of the predictive model.

Visible and near‐infrared image fusion based on information complementarity

AbstractImages with complementary spectral information can be recorded using image sensors that can identify visible and near‐infrared spectrum. The fusion of visible and near‐infrared (NIR) aims to enhance the quality of images acquired by video monitoring systems for the ease of user observation and data processing. Unfortunately, current fusion algorithms produce artefacts and colour distortion since they cannot make use of spectrum properties and are lacking in information complementarity. Therefore, an information complementarity fusion (ICF) model is designed based on physical signals. In order to separate high‐frequency noise from important information in distinct frequency layers, the authors first extracted texture‐scale and edge‐scale layers using a two‐scale filter. Second, the difference map between visible and near‐infrared was filtered using the extended‐DoG filter to produce the initial visible‐NIR complementary weight map. Then, to generate a guide map, the near‐infrared image with night adjustment was processed as well. The final complementarity weight map was subsequently derived via an arctanI function mapping using the guide map and the initial weight maps. Finally, fusion images were generated with the complementarity weight maps. The experimental results demonstrate that the proposed approach outperforms the state‐of‐the‐art in both avoiding artificial colours as well as effectively utilising information complementarity.

Intelligent Video Monitoring and Analysis System for Power Grid Construction Site Safety Using Wireless Power Transfer

Power grid construction significantly enhances power grid management and risk control. Inconsistent operations at construction sites can jeopardize grid stability and crew safety. Traditional power lines are less favored due to mobility limitations, while batteries add burden and impracticality. To address this, a wireless power transfer and video monitoring system is developed using RF technology and Yolo V3 model. This enables continuous monitoring and employee safety analysis. The system's detection performance is optimized using HPSO, surpassing existing methods in accuracy and speed. It ensures real-time monitoring and improves the detection of potential risk sources, crucial for construction site safety.

Football teaching and training based on video surveillance using deep learning.

The objective performance evaluation of an athlete is essential to allow detailed research into elite sports. The automatic identification and classification of football teaching and training exercises overcome the shortcomings of manual analytical approaches. Video monitoring is vital in detecting human conduct acts and preventing or reducing inappropriate actions in time. The video's digital material is classified by relevance depending on those individual actions. The research goal is to systematically use the data from an inertial measurement unit (IMU) and data from computer vision analysis for the deep Learning of football teaching motion recognition (DL-FTMR). There has been a search for many libraries. The studies included have examined and analyzed training through profound model construction learning methods. Investigations show the ability to distinguish the efficiency of qualified and less qualified officers for sport-specific video-based decision-making assessments. Video-based research is an effective way of assessing decision-making due to the potential to present changing in-game decision-making scenarios more environmentally friendly than static picture printing. The data showed that the filtering accuracy of responses is improved without losing response time. This observation indicates that practicing with a video monitoring system offers a play view close to that seen in a game scenario. It can be an essential way to improve the perception of selection precision. This study discusses publicly accessible training datasets for Human Activity Recognition (HAR) and presents a dataset that combines various components. The study also used the UT-Interaction dataset to identify complex events. Thus, the experimental results of DL-FTMR give a performance ratio of 94.5%, behavior processing ratio of 92.4%, athletes energy level ratio of 92.5%, interaction ratio of 91.8%, prediction ratio of 92.5%, sensitivity ratio of 93.7%, and the precision ratio of 94.86% compared to the optimized convolutional neural network (OCNN), Gaussian Mixture Model (GMM), you only look once (YOLO), Human Activity Recognition- state-of-the-art methodologies (HAR-SAM). This finding proves that exercising a video monitoring system that provides a play view similar to that seen in a game scenario can be a valuable technique to increase selection accuracy perception.

Development and Research of Safety Video Monitoring System for Loading and Unloading of Chemical Products in Railway Transportation

As the important position of chemical railroad transportation in modern logistics is becoming more and more prominent, its safety problem has attracted wide attention. To improve the safety of transporting chemicals by rail, this paper strives to develop an advanced safety monitoring system. The main background of this paper is the serious potential results of chemical products railroad transportation accidents which require an effective monitoring method to prevent. The purpose of this research is to design and realize an efficient and reliable video monitoring system to monitor the loading and unloading of chemicals transported by railroads in real-time. To achieve this goal, we adopted comprehensive methods, including video image recognition technology, data analysis technology, communication network technology, etc., which have significant meaning in preventing railroad accidents of chemical product transportation and safeguarding people’s life and property.

Security Threat Object Recognition System

Security Threat object recognition has emerged as a very advanced topic in the field of video surveillance community. Due to need of system has been proposed for video monitoring or surveillance system for public and private places to protect againt thept and recognise the suspicious object which is harmful for the society and the people. Because of these systems' complexity, researchers frequently handle the various stages of analysis—such as foreground segmentation, stationary object recognition, and abandonment validation—independently. The impact of each level of improvement on the overall performance of the system has not been investigated, despite the fact that each stage has seen gains. This is because the advancements are rarely applied across the entire pipeline. This study presents a thorough assessment of the state-of-the-art methods for each stage and formalizes the framework used by systems for abandoned object detection. Additionally, we develop a multi-configuration system that enables the selection of many options for every step, with the goal of identifying the combination that yields the best results. The scientific community has internet access to this multi-configuration. Keywords: Object detection, Surveillance, Threat Detection, Computer Vision, Deep Learning, YOLO, Image analysis, CNN, R-CNN, SSD

A video monitoring and computational system for estimating migratory juvenile fish abundance in river systems

AbstractDiadromous fishes migrate between marine and fresh waters for reproduction. For anadromous species, which spawn in freshwater, improved access to freshwater spawning and nursery habitats and ability of juveniles to emigrate to the ocean may support population recovery. Despite the potentially enormous influence of early life stage survival on adult population size, managers and scientists have limited capacity to assess numbers of juvenile anadromous fishes leaving freshwater ecosystems. Such data are critical for evaluating reproductive success and habitat suitability and have been identified as a top priority in anadromous fish research and management. We developed a state‐of‐the‐art underwater video and computational system to collect videos to estimate abundances and migration timing for juvenile river herring (Alosa pseudoharengus; Alosa aestivalis). We collected continuous video in the Monument River (Bourne, Massachusetts, USA) from June to November 2017. We trained three types of neural network models to detect and count fish in video frames and evaluated model performance by comparing human counts to model outputs. Our top model assessed presence and absence (F1 = 87%) and counted fish (counting error 9.4%) with an accuracy comparable to human counters (F1 = 88%). Our system's capability to collect accurate counts of emigrating juveniles will provide critical information that could be related to the numbers of spawning adults, system‐specific productivity, and spawning and nursery habitat suitability. Both the video collection system and computational model may be transferrable to other sites and for other species where tracking juvenile emigration may inform management efforts.

A Detecting Method of Users’ Reading State in Library via Modulated Light Illumination

ABSTRACT In order to overcome the shortcomings of the traditional methods for detecting the users’ reading state in a smart library building, a more effective detecting method of library users’ reading state is proposed. In this method, some posture characteristic parameters are put forward in order to represent the users’ reading state after analyzing the relationship between the users’ posture and their reading state. And then, one stripe light illuminating system of 3-D posture is designed by developing of a stripe projector and a video monitoring system, in which, the image of users’ posture with periodic stripe modulation is received on the video monitoring system. At last, two users’ posture stripe images are extracted by time sequence, one as a reference image and the other as a deformed image, and these images are superimposed by their pixel subtracting after extracting the stripes, so as to obtain a superimposed image that reflects the users’ posture variation characteristic. The users’ posture characteristic parameters can be gotten by calculating the area pixels and number of deformation regions of continuous pixels, and the changed pixels per unit time and per user based on the superimposed image. Therefore, the users’ reading state can be detected based on the relationship between the characteristic parameters and users’ reading state. This method can realize the accurate detecting of the reading users’ status and provide the basis to build the smart library.

An advanced multimodal driver-assistance prototype for emergency-vehicle detection

In the automotive industry, intelligent monitoring systems for advanced human-vehicle interaction aimed at enhancing the safety of drivers and passengers represent a rapidly growing area of research. Safe driving behavior relies on the driver’s awareness of the road context, enabling them to make appropriate decisions and act consistently in anomalous circumstances. A potentially dangerous situation can arise when an emergency vehicle rapidly approaches with sirens blaring. In such cases, it is crucial for the driver to perform the correct maneuvers to prioritize the emergency vehicle. For this purpose, an Advanced Driver Assistance System (ADAS) can provide timely alerts to the driver about an approaching emergency vehicle. In this work, we present a driver-assistance prototype that leverages multimodal information from an integrated audio and video monitoring system. In the initial stage, sound analysis technologies based on computational audio processing are employed to recognize the proximity of an emergency vehicle based on the sound of its siren. When such an event occurs, an in-vehicle monitoring system is activated, analyzing the driver’s facial patterns using deep-learning-based algorithms to assess their awareness. This work illustrates the design of such a prototype, presenting the hardware technologies, the software architecture, and the deep-learning algorithms for audio and video data analysis that make the driver-assistance prototype operational in a commercial car. At this initial experimental stage, the algorithms for analyzing the audio and video data have yielded promising results. The area under the precision-recall curve for siren identification stands at 0.92, while the accuracy in evaluating driver gaze orientation reaches 0.97. In conclusion, engaging in research within this field has the potential to significantly improve road safety by increasing driver awareness and facilitating timely and well-informed reactions to crucial situations. This could substantially reduce risks and ultimately protect lives on the road.

Design and implementation of an automated baby monitoring cradle

AbstractEver increasing global birth rate, increased working women ratio and innovative technologies motivate the engineering society to introduce novel ideas for the baby caring. Considering this, we present hardware implementation of an automatic cradle for smart and remote monitoring. In contrast to previous implementations, we present the design insights such as swing angle, required force and torque, arc length and speed of the cradle for the first time in the literature. Furthermore, to make the cradle more comfortable and smooth swing, we introduce a novel Slider Crank Mechanism to control the cradle swing for the first time in the literature. This provides flexibility, and improved energy efficiency, while maintaining controlled and predictable reciprocating motion. The proposed solution is equipped with a number of technological features, that is, cry detection based swing, collision avoidance, WiFi based remote video monitoring, wet detection and baby health monitoring system.

The Impact of Adding a 2-Way Video Monitoring System on Falls and Costs for High-Risk Inpatients.

We aimed to investigate the value of adding a video monitoring (VM) system with falls and costs for patients at high risk. We conducted a retrospective, historically controlled study of adults (≥18 y old) at high risk of fall admitted at the University of Miami Hospital and Clinics from January 1 to November 30, 2020 (pre-VM) and January 1 to November 30, 2021 (post-VM); in-person sitters were available in both periods. Fall risk assessment was conducted on admission and at every nursing shift; we defined patients as high risk if their Morse Fall Scale was ≥60. We conducted a multivariable logistic regression model to evaluate the association of period (pre- versus post-VM) with falls and performed a cost analysis. Our primary cohort consisted of 9,034 patients at high risk of falls, 4,207 (46.6%) in the pre-VM and 4,827 (53.4%) in the post-VM period. Fall rates were higher in the pre- than the post-VM periods (3.5% versus 2.7%, P = 0.043). After adjustment, being admitted during the post-VM period was associated with a lower odds of fall (odds ratio [95% confidence interval], 0.49 [0.37-0.64], P < 0.001). The median adjusted hospital cost (in 2020 dollars) was $1,969 more for patients who fell than for patients who did not (interquartile range, $880-$2,273). Considering start-up and ongoing costs, we estimate VM implementation to partly replace in-person monitoring has potential annual cost savings of >$800,000 for a hospital similar to ours. Video monitoring to augment in-person sitters is an effective fall prevention initiative for patients at high risk of falls, which is likely also cost-effective.

Image-Based Process Monitoring using Projective Nonnegative-Tensor Factorization

In this study, a novel process monitoring method using image data is presented. The new method is referred to as projective nonnegative-tensor factorization (PNTF). Video monitoring systems are increasingly being deployed in complex industrial processes such as manu-facturing processes, food industries, and pharmaceutical process, for extracting information from images to achieve the purpose of process monitoring. Traditional process-monitoring techniques fail to take full advantage of the rich information from color images due to the complex data characteristics including the high-dimensionality and correlation structures. The proposed PNTF is capable of modeling the high-dimensionality structure of red-green-blue data of color images using a tensor analysis technique. Moreover, in contrast to the traditional data-processing algorithms such as principal component analysis, linear discriminant analysis and so on, the PNTF is more applicable to analyzing image data for its nonnegative constraints on matrix factors. In addition, the PNTF allows us to extract features from both grayscale and color images, making process monitoring using multivariate control charts achiev- able. At last, based on the PNTF, methods for detecting and isolating process faults are developed and evaluated through a case study on a production process of electrically melting magnesia.

АКТУАЛЬНОСТЬ ПРОВЕДЕНИЯ МОНИТОРИНГА СЕЛЬСКОХОЗЯЙСТВЕННЫХ КУЛЬТУР И ХВОЙНОГО ПОДРОСТА В ВЕГЕТАТИВНЫЙ ПЕРИОД

Modern agriculture faces a number of challenges, including climate change, resource constraints and the need to increase productivity. In this context, the assessment of quantitative parameters of crop growth by technical means during the growing season becomes particularly relevant. The scientific approach to this problem is becoming a key tool for optimizing production and ensuring food security, sustainable development of forest ecosystems. (Research purpose) To substantiate the relevance of monitoring crops and undergrowth during artificial reforestation during the vegetative period. (Materials and methods) Video monitoring of agricultural crops, automated segment recognition, geospatial data, literature review, statistical analysis were used. (Results and discussion) The statistics and problems of existing measures for monitoring coniferous undergrowth after reforestation measures were analyzed. The methods of using video monitoring of agricultural crops, in particular, for analyzing the yield of corn shoots, were considered. The use of a video monitoring system to control the care of coniferous undergrowth in the first five years after reforestation was substantiated. Key limitations and difficulties were cited, including high labor costs, insufficient monitoring, lack of systematic approach, limited human labor and low speed and accuracy of data processing. It has been shown that these factors emphasize the need for innovations in methods of controlling the growth of crops and caring for coniferous young. (Conclusions) It was revealed that the video monitoring system of coniferous undergrowth is one of the solutions to the problems of monitoring the condition of forest areas. It was determined that it allows assessing the quality of planting work, determining the growth and health indicators of seedlings, monitoring the environment by indirect signs and, due to a high level of digitalization and automation, responding quickly to violations. It was stated that the assessment of the parameters of crop growth in real time with the use of technical solutions makes it possible to identify problems such as diseases and parasites at an early stage, which will improve the possibilities for their prevention.

Theoretical substantiation of the requirements for an intelligent video monitoring system for analyzing the physiological state of cows and predicting its changes

At the present stage of development of dairy farming, the main task facing the industry is the effective management of all processes that ensure the operation of a farm with a large livestock. This task will not be achievable without the use of digital intelligent production control and management systems, tracking the health of animals and their productivity. There are many systems that integrate into production and allow you to monitor the health of cows on a dairy farm. These systems are based on the identification, tracking and collection of information about the physiological state of animals through various sensors. However, all these systems are expensive, demanding to maintain, often fail due to aggressive operating conditions. The article considers the possibility of using an intelligent video monitoring system to analyze the physiological state of cows and predict its changes. To substantiate the requirements for an intelligent video monitoring system of the physiological state of cows, a mathematical model of the life of productive cows in the form of a graph of states is proposed. This method graphically displays the possible states of the system and their possible transitions from state to state. Having constructed a graph of states, relying on the central probability theorem for event flows, it is possible to determine the probability and intensity of state transitions using differential equations. For the functioning of the video monitoring system for analyzing the physiological state of cows and predicting its changes, the proposed mathematical model can be the basis of the system and be based on the collection and analysis of video information about animal behavior. When covering the entire territory of the animal's living space with video cameras, the intelligent video monitoring system will be able to provide early diagnosis of changes in the physiological state of the observed animals and issue a warning, which will allow for timely therapeutic and preventive measures.

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%.

Determination of basketball interference ball violation based on visual recognition technology electronic referee artificial intelligence algorithm

This article set up an electronic referee system based on monocular vision recognition. The system requires only two cameras to operate. By establishing a multi-camera real-time video monitoring system, the system cooperates with artificial intelligence automatic recognition judgment, overcomes many blind spots of human eye judgment, completes the judgment efficiently and accurately, improves the accuracy of the referee's ruling on basketball interference ball violation, and enhances the fairness and spectacle of the game. This project overcomes many blind spots of human eye judgement, efficiently and accurately completes the penalty, improves the accuracy of the referee's ruling on basketball interference ball violation, and enhances the fairness and spectacle of the game by establishing a multi-camera real-time video monitoring system with artificial intelligence automatic identification judgement.

A Markov Chain Model for Determining the Optimal Time to Move Pregnant Cows to Individual Calving Pens.

The use of individual calving pens in modern farming is widely recognized as a good practice for promoting good animal welfare during parturition. However, determining the optimal time to move a pregnant cow to a calving pen can be a management challenge. Moving cows too early may result in prolonged occupancy of the pen, while moving them too late may increase the risk of calving complications and production-related diseases. In this paper, a simple random walk type Markov Chain Model to predict the optimal time for moving periparturient cows to individual calving pens was proposed. Behavior changes such as lying time, standing time, and rumination time were analyzed using a video monitoring system, and we formulated these changes as the states of a Markov Chain with an absorbing barrier. The model showed that the first time entering an absorbing state was the optimal time for a pregnant cow to be moved to a calving pen. The proposed method was validated through a series of experiments in a real-life dairy farm, showing promising results with high accuracy.

An inexpensive low-cost video monitoring system for automated recording of behavior and ecological interactions

Active, real-time observation of behavior is a time-consuming task, which is heavily resource-limited. At the same time, simultaneous observation of several individuals is often paramount to increase statistical rigor and eliminate potential temporal or environmental bias, especially in natural settings. This paper describes a low-cost video recording system created by using “off-the-shelf” components. The system is easy to use and can automatically record a wide variety of behavior and related ecological interactions and evolutionary processes. The system is sensitive enough to record the behavior of a broad range of animals from planarians, and small insects to humans. It can also be used to measure the behavior of plants. The system will also work during daylight hours or at night and can run continuously and autonomously for 48 hours, or longer if the video capture is motion-triggered or if bigger capacity batteries and data storage facilities are used.

Detection efficiency of adult Pacific lamprey passage counts at Leaburg Dam and upstream distribution in the McKenzie River (Oregon, USA)

AbstractAdult Pacific lamprey (Entosphenus tridentatus) were counted using consistent methodology since 2005 with a video monitoring system as they passed Leaburg Dam (McKenzie River, Oregon, USA) en route to upstream spawning areas. In this study we evaluated the detection efficiency of the video system and upstream distribution of Pacific lamprey using the video counts of lamprey passage (herein, “dam counts”), passive integrated transponder (PIT) tags, and radio telemetry. In 2019–2020 we collected, tagged and tracked 32 adult lamprey (4 from the McKenzie River and 28 that were translocated from Willamette Falls). All fish were tagged and released into the tailrace of Leaburg Dam in June 2019. Oregon Department of Fish and Wildlife employees and volunteers from the local community conducted mobile radiotracking above and below the dam in drift boats (114 mainstem river kilometers) and on foot (several tributaries). The estimated detection efficiency for dam counts was 92% (95% confidence interval: 67%–99%). Fifty percent (16 of 32) of the tagged lamprey passed the dam, including 13 of 28 that were translocated. Thirty‐seven percent (6 of 16) of lamprey that passed Leaburg Dam were detected in a restored reach of the South Fork McKenzie, 32.6 river kilometers upstream of the dam.

Nurses' Experience Implementing an Automated Video Monitoring System to Decrease the Risk of Patient Falls during a Global Pandemic.

Healthcare is a complex sociotechnical system where information systems (IS) and information technology (IT) intersect to solve problems experienced by patients and providers alike. One example of IS/IT in hospitals is the Ocuvera automated video monitoring system (AVMS), which has been implemented in more than 30 hospitals. The purpose of this study was to evaluate nurses' attitudes toward AVMS implementation over time as they received the training program developed for this intervention. Consistent with the job demands-resources (JDR) model, we found that perceptions of AVMS usefulness increased over time and were positively associated with perceptions of social influence and behavioral control. These results were consistent with our finding that there was a significant decrease in the risk of unassisted falls from the bed from baseline to intervention. Leaders in hospital systems and healthcare organizations may want to consider implementing an AVMS as researchers continue to test, verify, and demonstrate the effectiveness of these interventions for improving patient well-being.