Intelligent Video Surveillance Technology Research Articles

Hybrid No-Reference Quality Assessment for Surveillance Images

Intelligent video surveillance (IVS) technology is widely used in various security systems. However, quality degradation in surveillance images (SIs) may affect its performance on vision-based tasks, leading to the difficulties in the IVS system extracting valid information from SIs. In this paper, we propose a hybrid no-reference image quality assessment (NR IQA) model for SIs that can help to identify undesired distortions and provide useful guidelines for IVS technology. Specifically, we first extract two main types of quality-aware features: the low-level visual features related to various distortions, and the high-level semantic information, which is extracted by a state-of-the-art (SOTA) vision transformer backbone. Then, we fuse these two kinds of features into the final quality-aware feature vector, which is mapped into the quality index through the feature regression module. Our experimental results on two surveillance content quality databases demonstrate that the proposed model achieves the best performance compared to the SOTA on NR IQA metrics.

Research on the Water Quality Intelligent Monitoring System of River Gushing Based on Federal Learning

Intelligent video surveillance technology is increasingly widely used in social production and life and plays an important role in economic development and environmental protection. This paper focuses on the field of water quality monitoring, and aims at the problems of traditional river gushing including difficulty of water quality monitoring, poor timeliness and insufficient automation, and proposes the intelligent water quality monitoring method from the perspective of intelligent image treatment. A federated learning system combined with a NVIDIA Jetson TX2 edge nodes is constructed by HD cameras deployed at various monitoring points of the gushing. On this basis, based on the convolutional neural network technology, the identification and monitoring of the river gushing water quality can effectively reduce the operation amount and water quality monitoring automation degree under the case of ensuring the accuracy.

Engineering design of electronic fence system based on intelligent monitoring and wireless local area network

With the establishment of the economic New Area, preliminary results have been achieved in the construction of the business environment, and some experience has been accumulated, but there are still many problems. The most important of these is the issue of business safety. The security system in commercial areas generally adopts manual shifts. This management method is increasingly unsuitable for urban development. Therefore, the unattended electronic fence system emerges from time to time, but its security has become an urgent problem to be solved. This article designs the electronic fence system project based on intelligent video surveillance and wireless local area network technology. This article designs the electronic fence system based on intelligent video surveillance and wireless local area network technology. First, design a pulsebased electronic fence system. Under the control of the FPGA, by sending pulses to the electronic fence, the frontend detection fence subsystem can detect the pulse signals, and implement the pulse signals to the FPGA through wireless local area network technology. After feature extraction and pulse signal recognition, determine whether an abnormal situation has occurred. Secondly, an intelligent video surveillance system based on wireless local area network is designed to record the surrounding video information 24 h, and use related target detection technology to locate and record abnormal conditions. The experimental results show that the design system canwell judge the occurrence of abnormal conditions and record the abnormal conditions in realtime, which can ensure the safety.

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 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.

Application of Vision-based Safety Warning System to Haeundae Beach, Korea

Lee, J.; Park, J.; Kim, I., and Kang, D.Y., 2019. Application of vision-based safety warning system to Haeundae Beach, Korea. In: Lee, J.L.; Yoon, J.-S.; Cho, W.C.; Muin, M., and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 91, pp. 216-220. Coconut Creek (Florida), ISSN 0749-0208.Between 2012 and 2017, the number of drowning incidents in South Korean beaches has been on the rise. To prevent drowning accidents, huge number of lifeguards are hired to operate lifesaving activities. This burdens municipal governments in the budget. Nevertheless, because of the limitation in these resources as well as equipment, it is difficult to maintain the effective safety management. Nighttime incidents caused by entering into the water in the absence of lifeguard makes safety management become more critical. In order to resolve the issue of limited numbers of lifeguards and equipment in the aquatic facilities including beaches, it is necessary to develop automated detecting of high profile of drowning and warning systems that support lifeguarding and safety management. Moreover, to provide and maintain safe beach environment in accordance with international standards and prevent injuries and life-threatening incidents, it is essential to develop relevant reliable systems. Among these systems is the application of an intelligent video surveillance technology, the CCTV, for establishing a safe beach environment. The CCTV technology can help in developing a system that detects swimmers and beachgoers approaching dangerous zones and takes proper action. By integrating life-saving technologies that can be applied during both daytime and nighttime, which issue warnings and generate responses when swimmers enter the water at night, it enables to overcome vulnerabilities of other disaster control systems and ensure efficient safety monitoring round the clock.AELIS (Aquatic Environment Life-saving Integrated System) is designed to monitor the high profiles of drowning and the swimmers approaching the swimming boundary lines in real time in order to prevent accidents in the water environment and maintain high standard of the safety. It also detects the swimmers who attempt to get in the water without lifeguards at night, which has high possibility of drowning.

An online graph-based anomalous change detection strategy for unsupervised video surveillance

Due to various accidents and crime threats to an unspecified number of people, many surveillance technologies have been studied as an interest in individual security continues to increase throughout society. In particular, intelligent video surveillance technology is one of the most active research areas in the field of surveillance; this popularity has been spurred by recent advances in computer vision/image processing and machine learning. The main goal is to automatically detect, recognize, and analyze objects of interest from collected sensor information and then efficiently extract/utilize this useful information, such as by detecting abnormal events or intruders and recognizing objects. Anomalous event detection is a key component of security, and many existing anomaly detection algorithms rely on a foreground subtraction process to detect changes in the foreground scene. By comparing input image frames with a reference image, changed areas of the image can be efficiently detected. However, this technique can be insensitive to static changes and has difficulties in noisy environments since it depends on a reference image. We propose a new strategy for improved dynamic/static change detection that complements the weak points of existing detection methods, which have low robustness in noisy environments. To achieve this goal, we employed a self-organizing map (SOM) for data clustering and regarded the cluster distribution of neurons, represented by the weight of the optimized SOM, as a directed graph problem. We then applied the shortest path algorithm to recognize anomalous events. The real-time monitoring capability of the proposed change detection system was verified by applying it to self-produced test data and the CDnet-2014 dataset. This system showed robustness against noise that was superior to other surveillance systems in various environments.

Retracted: Smart city framework based on intelligent sensor network and visual surveillance

SummaryIn the field of urban development, the construction of new smart cities has received the attention of the government and society. In the process of promoting urban intelligence, urban road lighting, intelligent lighting, etc, have gradually received attention, while the lighting source and control methods have been greatly improved. Smart cities portray a very good vision for development for human beings. Traditional video surveillance only provides simple functions such as video capture, storage, and playback, which are difficult to use as an early warning and alarm. To ensure that abnormal behaviors are monitored in real time and effective measures are taken in a timely manner, monitoring personnel are required to monitor the video at all times. A huge monitoring network will generate massive video data. How to efficiently extract useful information from these massive data is a problem to be solved by intelligent video surveillance technology. This paper starts with the concept of top‐level design of smart city construction and analyzes the meaning and content of top‐level design. This paper summarizes some of the misunderstandings in the current top‐level design process and provides suggestions for the top‐level design.

Crowd behaviors analysis and abnormal detection based on surveillance data

Crowd analysis and abnormal trajectories detection are hot topics in computer vision and pattern recognition. As more and more video monitoring equipments are installed in public places for public security and management, researches become urgent to learn the crowd behavior patterns through the trajectories obtained by the intelligent video surveillance technology. In this paper, the FCM (Fuzzy c-means) algorithm is adopted to cluster the source points and sink points of trajectories that are deemed as critical points into several groups, and then the trajectory clusters can be acquired. The feature information statistical histogram for each trajectory cluster which contains the motion information will be built after refining them with Hausdorff distances. Eventually, the local motion coherence between test trajectories and refined trajectory clusters will be used to judge whether they are abnormal.

Performance evaluation of an intelligent video surveillance system – A case study

Intelligent video surveillance (IVS) technology is on the cusp of moving from early adopters to general acceptance in several markets such as security and business intelligence. This transition has been made possible by embedding computer vision technologies directly into video devices such as cameras, encoders, routers, DVRs, NVRs, and other video management and storage hardware. For this technology to be successful, it is crucial that IVS systems can be deployed easily, without requiring computer vision expertise to customize them for every installation; and that the systems work robustly in a wide range of environments. One of the key enablers to achieve this goal is proper testing. This paper discusses some of the major challenges involved and provides a case study for addressing the problem. One of the key concepts is utilizing fuzzy evaluation to handle boundary conditions.