Intelligent Video Monitoring System Research Articles

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.

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.

Working Condition Monitoring System of Substation Robot Based on Video Monitoring

In order to realize intelligent video surveillance of substation, this paper presents a substation robot working state monitoring system based on video surveillance. In this paper, the intelligent monitoring system of substation robot working state is constructed by combining virtual reality technology. Through the cooperation of each unit in the system, the real-time monitoring of substation robot working state and the early warning of abnormal working state are realized. The system can transmit video data at a high frame rate to ensure the timeliness of video data transmission and clearly and smoothly present the virtual scene of substation robot work. It can realize the high-precision positioning measurement of the robot under different distances and speeds, obtain the working dynamic trajectory of the robot according to the measurement results, analyze its working state, and give an early warning in case of abnormal working state, so as to achieve the purpose of intelligently monitoring the working state of the substation robot. The experimental results show that each group of video data transmission frame rate of the system can reach 29 f/s and is relatively stable. The average transmission frame rate is 30.62 f/s. The video transmission frame rate is high, which can ensure the timeliness of video transmission. Conclusion. The designed substation intelligent video monitoring system has good output stability and reliability.

Intelligent video surveillance of containers in the view of electromagnetic compatibility tests

Abstract: The article presents the results of field tests of the intelligent video monitoring system of railway wagons, own name IMW. The IMW device was created as part of a project financed by the National Center for Research and Development, under Measure 4.1 of the Intelligent Development Operational Program 2014-2020 POIR.04.01.04-00-0157/17-00. The leader of the project is MobileMS, while the Railway Institute conducted various research works from 2019 to 2021 together with other institutions (consortium partners) i.e. STIPENDIUM Institute of Science and Technology and Kodegenix. The Automation and Telecommunications Laboratory of the Railway Institute realized part of the electromagnetic compatibility tests. The measurements were carried out in laboratory and field conditions in order to reduce as much as possible the level of generated interference from the IMW device. Tests were carried out on the IMW device mounted by means of metal clamps on the hydraulic distribution block of the WM-15A type motorized bogie. EMC and interference test specialists of the Railway Institute developed test templates according to which the planned measurements were carried out. Keywords: Electromagnetic Compatibility; Diagnostics; Radiated emission

Research on small moving target detection algorithm based on complex scene

In recent years, small moving target detection based on complex scenes is widely used in many fields, such as computer vision, image processing and pattern recognition. It is widely used in military navigation, intelligent video monitoring system, video retrieval and other fields. Due to the dynamic background in the complex scene, the moving object has the characteristics of long distance, small area, unclear texture and color features, and partial loss of detail features. Therefore, how to extract small moving objects quickly, accurately and completely from complex scenes is the key of subsequent image analysis and processing. Aiming at the problem of strong background noise in more complex scenes, this paper proposes a neighborhood frame search denoising algorithm based on time consistency, which can effectively remove the strong background noise disturbance. The experimental results show that the algorithm can effectively remove strong background noise.

Low-light images enhancement method in coal mine based on knowledge distillation

In order to solve the problems of low illumination and poor image quality in video monitoring systems caused by complex lighting conditions in underground mines, a low-light image enhancement method based on knowledge distillation is proposed. By establishing Net-S (student network) and Net-T (teacher network) for feature simulation, this method improves the image reconstruction ability of the model while transferring knowledge and improves the network efficiency and image enhancement effect at the same time. The improved RIR (residual in residual blocks) module is added to improve the feature extraction ability of the model. At the same time, the loss function for low illumination image characteristics is designed to improve the accuracy of the network model in image reconstruction. Compared with the traditional knowledge distillation network model, Net-T and Net-S of the model output different images respectively. Net-T extracts features from high-quality images. Net-S simulates features under the guidance of Net-T, and finally obtains enhanced images. The experimental results show that the model can effectively remove part of the noise, improve the brightness and quality of the image, and improve the visual effect of the image. Moreover, the model has good generalization ability. Compared with CLAHE and LIME, the absolute cumulative change of the normalized objective index of this method is increased by 42.21%, 7.47% and 30.18% respectively. In view of the low illumination image enhancement of the mine, this method has certain advantages, which can significantly improve the overall brightness of the image, reduce the noise, and is more in line with the human visual perception. To a certain extent, it meets the needs of the construction of intelligent mine video monitoring system.

Implementing an intelligent video monitoring system to detect falls of older adults at home: a multiple case study

Purpose Older adults are at a high risk of falling. The consequences of falls are worse when the person is unable to get up afterward. Thus, an intelligent video monitoring system (IVS) was developed to detect falls and send alerts to a respondent. This study aims to explore the implementation of the IVS at home. Design/methodology/approach A multiple case study was conducted with four dyads: older adults and informal caregivers. The IVS was implemented for two months at home. Perceptions of the IVS and technical variables were documented. Interviews were thematically analyzed, and technical data were descriptively analyzed. Findings The rate of false alarms was 0.35 per day. Participants had positive opinions of the IVS and mentioned its ease of use. They also made suggestions for improvement. Originality/value This study showed the feasibility of a two-month implementation of this IVS. Its development should be continued and tested with a larger experimental group.

Confused-Modulo-Projection-Based Somewhat Homomorphic Encryption—Cryptosystem, Library, and Applications on Secure Smart Cities

With the development of cloud computing, the storage and processing of massive visual media data has gradually transferred to the cloud server. For example, if the intelligent video monitoring system cannot process a large amount of data locally, the data will be uploaded to the cloud. Therefore, how to process data in the cloud without exposing the original data has become an important research topic. We propose a single-server version of somewhat homomorphic encryption cryptosystem based on confused modulo projection theorem named CMP-SWHE, which allows the server to complete blind data processing without <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">seeing</i> the effective information of user data. On the client side, the original data is encrypted by amplification, randomization, and setting confusing redundancy. Operating on the encrypted data on the server side is equivalent to operating on the original data. As an extension, we designed and implemented a blind computing scheme of accelerated version based on batch processing technology to improve efficiency. To make this algorithm easy to use, we also designed and implemented an efficient general blind computing library based on CMP-SWHE. We have applied this library to foreground extraction, optical flow tracking, and object detection with satisfactory results, which are helpful for building smart cities. We also discuss how to extend the algorithm to deep learning applications. Compared with other homomorphic encryption cryptosystems and libraries, the results show that our method has obvious advantages in computing efficiency. Although our algorithm has some tiny errors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-6}$ </tex-math></inline-formula> ) when the data is too large, it is very efficient and practical, especially suitable for blind image and video processing.

RETRACTED ARTICLE: A City Monitoring System Based on Real-Time Communication Interaction Module and Intelligent Visual Information Collection System

With the rapid development of society, the improvement of material level and the current situation of the large-scale population flow in China, the awareness of security is becoming more and more important in people’s life. With the rapid development of image processing and computer vision technology, people try to analyze, process and understand the collected video image automatically without human intervention. The intelligent video monitoring system collects video signals of interested objects in a dynamic scene through a camera, and processes and analyzes image information by a computer. Only by establishing a reasonable and effective urban video monitoring management system can government departments find out problems in the first time. The traditional highway monitoring and commanding traffic scheduling system based on GIS, which can obtain road traffic information and conduct traffic scheduling by remote sensing, has the disadvantage of poor effect on traffic scheduling. In this paper, real-time communication technology and computer vision acquisition technology are used to build a city monitoring system. The experimental results show that this method has strong timeliness and good monitoring effect. Compared with the state-of-the-art methodologies, the proposed framework is efficient and accurate.

A working human abnormal operation recognition approach based on the deep multi-instance sorting model

Modeling abnormal behavior detection in the intelligent video monitoring system for recognition or detection of special event has attracted significant research interest in recent years. In order to achieve more effective recognition of abnormal behavior detection of the intelligent video surveillance system, this paper proposes a working human abnormal operation recognition approach based on deep multi-instance sorting model. Firstly, the uncut long video is sparse to obtain normal and abnormal behavior video segments, and the RGB and optical flow features in the segment are extracted by the deep convolution network. Video feature vectors are obtained by the consensus function and feature extractor. Then, multi-instance sorting learning is used to assign abnormality scores between 0 and 1 to feature vectors. When the abnormal values of abnormal packets are higher than those of normal packets, the abnormality score is returned and the high abnormality score is determined as an abnormal behavior. The experiments on the open THUMOS14 data set and our own XAGCWD data set using CUDA GPU accelerated computing to demonstrate that our approach improves the recognition accuracy about 10.8% and high accuracy of abnormal detection. The main purpose of this research is to apply the model proposed in this paper to the intelligent workshop behavior monitoring system to effectively realize the safety management of workshop personnel.

An intelligent video-monitoring system to detect responsive behaviours associated with Alzheimer’s disease and related disorders

An intelligent video-monitoring system to detect responsive behaviours associated with Alzheimer’s disease and related disorders

Conditions and ethical challenges that could influence the implementation of technologies in nursing homes: A qualitative study

To explore the conditions that may influence the implementation of an interactive mobile application (app) and an intelligent videomonitoring system (IVS) in nursing homes (NHs) and the ethical challenges of their use. There is a lack of knowledge about implementing technologies in NHs and the ethical challenges that might arise. In past studies, nursing care teams expressed the need for technologies offering clinical support. Technologies like an IVS and an app could prove useful in NHs to prevent and manage falls and responsive behaviours. An exploratory qualitative study was conducted with care managers, family caregivers and formal caregivers in five NHs. Each participant was shown a presentation of a potential app and a short video on an IVS. It was followed by an individual semi-structured interview. A conventional contentanalysis was performed. Potential users found it would be possible to implement these technologies in NHs even if resistance could be expected. To facilitate adoption and achieve clinical benefits, the implementation of technologies should be pilot-tested, and coaching activities should be planned. Ethical risks were considered already present in NHs even without technologies, for example, risks to privacy. Strategies were proposed, for instance, to adapt the code of ethics and procedures. Some potential prejudices about the interest and abilities of older staff, nurses' aides, and family caregivers to use technology were identified. Through rigorous and ethical implementation, technologies supporting clinical care processes could benefit older people living in NHs, as well as their relatives and the staff. Various strategies are proposed to successfully implement technologies. Effort should be made to avoid prejudices during implementation, and procedures should be adapted to mitigate possible ethical challenges.

Discriminative Correlation Filter for Long-Time Tracking

Abstract Object tracking is a very important step in building an intelligent video monitoring system that can protect people’s lives and property. In recent years, although visual tracking has made great progress in terms of speed and accuracy, there are still few real-time high-precision tracking algorithms. Although discriminative correlation filters have excellent performance in tracking speed, there are deficiencies in handling fast motion. This leads to the inability to achieve long-term stable tracking results. The long-time tracking with discriminative correlation filter (LT-DCF) was proposed to solve these deficiencies. We use larger size detection image blocks and smaller size filters to increase the proportion of real samples to solve the boundary effects of fast motion. And we combine the histogram of oriented gradient (HOG) feature detection and scale-invariant feature transform (SIFT) key point detection to solve the obstacles caused by scale variations. The detector with deep feature flow is then incorporated into the tracker to detect key frames to improve tracking accuracy. This method has achieved more than 75% of the distance accuracy and 70% of the overlapping success rate on the VOT2015 and VOT2016 datasets, and the stable tracking video length can reach 6895 frames.

An intelligent video-monitoring system to detect falls: a proof of concept

Purpose To face the challenges raised by the high incidence of falls among older adults, the intelligent video-monitoring system (IVS), a fall detection system that respects privacy, was developed. Most fall detection systems are tested only in laboratories. The purpose of this paper is to test the IVS in a simulation context (apartment-laboratory), then at home. Design/methodology/approach This study is a proof of concept including two phases: a simulation study to test the IVS in an apartment-laboratory (29 scenarios of activities including falls); and a 28-day pre-test at home with two young occupants. The IVS’s sensitivity (Se), specificity (Sp), accuracy (A) and error rate (E) in the apartment-laboratory were calculated, and functioning at home was documented in a logbook. Findings For phase 1, results are: Se =91.67 per cent, Sp =99.02 per cent, A=98.25 per cent, E=1.75. For phase 2, the IVS triggered four false alarms and some technical dysfunctions appeared (e.g. computer screen never turning off) that are easily overcome. Practical implications Results show the IVS’s efficacy at automatically detecting falls at home. Potential issues related to future installation in older adults’ homes were identified. This proof of concept led to recommendations about the installation and calibration of a camera-based fall detection system. Originality/value This paper highlights the potentialities of a camera-based fall detection system in real-world contexts and supports the use of the IVS to help older adults age in place.

An intelligent video monitoring system: From an idea to the home implementation to detect falls

An intelligent video monitoring system: From an idea to the home implementation to detect falls

The Intelligent Video Monitoring System Based on Cloud Storage and Safety

Cloud storage has braked through the limit of traditional methods to store the performance. It is able to connect the large number of various types of storage devices on the network, and thus form a very powerful storage capacity, which has successfully solved the problem of massive video data storage in intelligent video surveillance system. This article designs the intelligent video monitoring system based on cloud storage architecture, which can realize realtime video monitoring, video storage, device access, system management and other functions, all video data transmission, processing, storage management are processed by computer resources in management of video cloud platform , which greatly simplifies the application link, and saves the cost of customers. Cloud storage shared encoded video encryption and method for accessing policy updates used by this article have reached the requirement of cloud storage video security, and improve the efficiency of the access control policy updates significantly.

Surveillance video face recognition with single sample per person based on 3D modeling and blurring

Video surveillance has attracted more and more interests in the last decade, video-based Face Recognition (FR) therefore became an important task. However, the surveillance videos include many vague non-frontal faces especially the view of faces looking down and up. As a result, most FR algorithms would perform worse when they were applied in surveillance videos. On the other hand, it was common at video monitoring field that only Single training Sample Per Person (SSPP) is available from their identification card. In order to effectively improve FR for both the SSPP problem and the low-quality problem, this paper proposed an approach to synthesis face images-based on 3D face modeling and blurring. In the proposed algorithm, firstly a 2D frontal face with high-resolution was used to build a 3D face model, then several virtual faces with different poses were synthesized from the 3D model, and finally some degraded face images were constructed from the original and the virtual faces through blurring process. At last multiple face images could be chosen from frontal, virtual and degraded faces to build a training set. Both SCface and LFW databases were employed to evaluate the proposed algorithm by using PCA, FLDA, scale invariant feature transform, compressive sensing and deep learning. The results on both datasets showed that the performance of these methods could be improved when virtual faces were generated to train the classifiers. Furthermore, in SCface database the average recognition rates increased up to 10%, 16.62%, 13.03%, 19.44% and 23.28% respectively for the above-mentioned methods when virtual view and blurred faces were taken to train their classifiers. Experimental results indicated that the proposed method for generating more train samples was effective and could be considered to be applied in intelligent video monitoring system.

Application of Intelligent Video Monitoring System in Electric Power Construct

In allusion to the problem of the blind zone in present intelligent video monitoring system, an intelligent video monitoring system with auditory function is proposed in this article. Specifically, the sound localization of the microphone array is firstly adopted to detecting the target direction, and then the camera is driven to turn according to the target position. The simulation result shows that such system can improve target localization and tracking accuracy and effectively reduce false report and missing report, thus to have certain application value in modern security and protection field.

Perception et réceptivité des proches-aidants à l'égard de la vidéosurveillance intelligente pour la détection des chutes des aînés à domicile.

To address the issue of falls, which are increasing as the population ages, an intelligent video-monitoring system is being developed. The aim of the study is to explore caregivers' perceptions of and receptiveness to a prototype of this fall detection system. A cross-sectional mixed-method study was carried out with individual interviews of 18 caregivers. Statistical frequencies and content analysis were conducted (SPSS and N'Vivo). The results show that most participants (n = 15/18) liked the intelligent video-monitoring system and were willing to use it. They would worry less if they could be alerted if a care recipient fell, but they were concerned about privacy and cost. Participants had a positive perception of the system and expressed their wishes regarding the kind of alert and the person to contact in case of a fall.

The Design of Intelligent Video System Based on DM8168

With the development of the times and the increasing mobility of the population, the situation of public environmental safety is becoming more and more serious. So an intelligent video monitoring system is designed in order to better carry out constantly effective monitoring for the public environment. In this paper, the IT Company DM8168 as the core of hardware designs an intelligent video surveillance system to realize the function of video capture, encoding, transmission, storage and so on. Besides, it will carry out motion detection in the aspect of intelligence. The main tasks can be completed as follows, first, architecture and front-end network camera hardware design of intelligent video system requires single-stream network. Second, the design of intelligent video system software, which is to complete the overall software design flow chart. Clients using the media player on the PC front-end network camera based on Davinci platform development. Third, algorithm design of intelligent software system is to study of the moving object detection algorithm and provide the algorithm effect diagram.