CCTV Cameras Research Articles

Home Door Security System with Face Recognition Using ESP 32 Cam

The development of science and technology, especially in the fields of Electronics and Informatics, is expected to enhance room or home security systems, particularly home door security systems. Many door security technologies have been developed to prevent and ensure the comfort of homeowners when they leave their homes. Some commonly used door security systems include CCTV cameras, fingerprint sensors, voice sensors, and RFID. However, with technological advancements, these security systems have shown vulnerabilities, such as frequent errors in fingerprint and voice sensors. Based on this, to create a more secure and innovative home door security system, the authors developed a security system with face detection and a remote warning notification system using Telegram. The researchers created a door security system using the ESP 32-CAM for face detection and as a controller for the Internet of Things (IoT) system. Additionally, a manual system using a keypad for password input was also created. The results of this study include the accuracy and speed of face recognition and the speed of sending security system warning information through the Telegram application. Keywords— Door Lock System, Face Recognition, ESP32 Camera, Microcontroller, Internet of Things.

Effect of Social Isolation on Behavioural Response of Murrah Buffalo Calves Towards Novelty

Background: During the production cycle, dairy animals in intensive production systems are exposed to various challenging events including novel stimuli (e.g., handlers, food and group-mates). The welfare of an individual animal depends on the ability to cope with environmental challenges. Novelty is generally associated with negative emotions however; individual behavioural and physiological responses toward challenging events can be very variable. So, studying behavioural response of calves towards novelty can help us address the welfare needs of the buffalo calves in a holistic manner. Methods: For this study, 24 Murrah buffalo calves were selected at birth and randomly allotted to four groups (six calves each) comprising of SG group (restricted suckling and group housed), SI group (restricted suckling and individually housed), WG group (weaned at birth and group housed) and WI group (weaned at birth and individually housed) for a period of three months. All calves were trained with bottle feeding from day old of age. At 9th week of age, the behavioural response of the calves to novelty was tested in two different novel situations i.e. novel object test and social novelty test using CCTV camera. Result: The results suggest that buffalo calves separated from mother and housed individually showed increased behavioural responses characterized by increased time in decision making, higher standing idle time and more fearful to an unfamiliar calf compared to other groups. It can be concluded that social isolation of the buffalo calves increases fearfulness to novelty hence reducing their welfare in future.

Evaluating the Role of Electronic Security Surveillance in Enhancing Safety in Secondary Schools: A Case Study of Trans Nzoia County, Kenya

Secondary school safety is critical to sustaining comparable academic performance outcomes. However, there are so many threats that affect the safety of schools that make up this necessity throughout the world. Security challenges in secondary schools in Kenya includes fires, theft and radicalization have been witnessed in school. Nevertheless, the application of electronic security surveillance systems still remains rather low. They work under very high risks and many schools in Trans Nzoia County are especially vulnerable leaving the school community at risk. This research aimed at evaluating the extent to which these systems enhance safety. Based on Situational Crime Prevention Theory and Contingency Theory, the study adopted descriptive survey research design and targeted 14 second schools with previous security threats and deployed with surveillance systems. Participants were 14 principals, 14 boarding teachers, 14 security personnel, 70 students- 10 students from each of the 7 schools, County Director of Education, and the Chief Fire Officer. The sample size for the study was 128 respondents. Data was collected using questionnaires, interview schedules and Focus Group Discussions. The data was analysed through using Social Package for Social Sciences version 25. Regarding electronic surveillance system improvement of school security, 80.8 percent of respondents supported its effectiveness. When national ICT and school safety policies had been included, it was found that the improvement of these systems for the safety of schools has proved 40.9% different (R² = 0.409, F (2, 75) = 26.003, p = 0.000). Of all the forms of surveillance, the study noted that CCTV cameras were the most widely used in secondary schools. That being said, the efficiency of their use in achieving safety objectives was somewhat hampered by operational obstacles. The study therefore suggests that while emphasizing on the use of additional technologies like fire alarms, burglar alarms, Metal detectors and Biometric systems, the school management boards in collaboration with Ministry of education and ICT should devise way of extending the use of such gadgets. Secondly, to guarantee full coverage of the school premises, it is necessary to step up investment in electronic security systems, and improve the support of personnel pertaining to the detection of threats.

Evaluative Performance Program E-Tilang di Simpang Raya Kota Surabaya

ETLE (Electronic Traffic Law Enforcement) is a set of electronic devices that use Automatic License Plate Recognition (ANPR) technology in the form of cameras that automatically detect motorized vehicles and cars with traffic violations E-TLE system, which is part of law enforcement efforts, this system aims to reduce problems caused by traffic violations, such as congestion and accidents, and ensure order on the highway. The e-Tilang system was pioneered by the East Java Regional Police, especially the Surabaya Police, which has this innovation as a crucial role in improving safety and regularity of electronically based traffic, the implementation of ETLE tickets in Surabaya City has increased in 2022 and 2023 compared to 2021. Which in 2021 the ETLE program was intensified due to the Covid-19 pandemic in order to increase the efficiency of technology-based services. The ETLE camera system uses CCTV cameras that are monitored directly by Police Officers with a total of 22 monitoring camera points in several parts of Surabaya City. where each officer oversees 4 areas with 8 CCTVs on the monitoring screen. Although it has had a positive impact on the implementation of ticketing, there are many things that should be evaluated from the process of running E-TLE at Surabaya Police Station. Mainly in the process of implementing and providing maximum fines, as well as the limited number of employees and time in ETLE supervision. So that the Process on Performance and the Facilities used should be evaluated in the running of the ETLE system in Surabaya City. The method used in this research is descriptive qualitative. The focus of this research is to find out the results of the Evaluation of the E-Ticket Policy Program (E-TLE) at the Surabaya City Road Intersection The theory used in the research is Gupta & Jana's Evaluative performance theory. There are 4 indicators from this theory, namely total costs and fines, quality of planning and control, quality of decision making and system characteristics. The data collection technique in this study is by conducting interviews, observation and documentation. In this research, from these 4 indicators, some are quite appropriate, but there are still many shortcomings that should be evaluated from the system and the quality of performance of the Surabaya police litas unit. The performance evaluation of the E-Tilang program in Surabaya shows success in managing total costs and fines, the quality of planning and control, and the quality of decision making. This program includes the use of 40 CCTV camera units and fiber optic networks that support the effectiveness of law enforcement, as well as the management of fair fines. Careful planning and better coordination after the evaluation ensured efficiency in recording violations using gadgets and the E-Tilang application.

CCTV image‐based classification of blocked trash screens

AbstractThis study introduces image‐based classification techniques to identify whether trash screens in urban rivers are blocked. The study obtained 755 images from a CCTV camera surveying a trash screen located on an urban river at Tongwynlais in Cardiff. Manual quality control reduced the dataset to 577 images, labelled as either blocked (80%) or unblocked (20%). The performance of a logistic regression for classification of images was investigated using three different subsets of the labelled images: (1) the original dataset, (2) a balanced but under‐sampled dataset with equal number of blocked and unblocked images, and (3) an augmented dataset with an equal number of blocked and unblocked images using Gaussian noise augmentation to increase the number of unblocked images. Results show that our data‐augmentation method enhanced model accuracy by 8%, successfully classifying images as blocked or unblocked with an accuracy of 88%; by overcoming the bias in the dataset these results also highlight potential solutions to overcome the challenges of operating this methodology across a network of cameras. This enables authorities in both data rich and data scarce regions the ability to take advantage of machine learning to open up the next generation of a distributed, data‐driven flood warning systems, protecting people, infrastructure and the environment.

Choosing the Best Installation Paths In the Development of Urban CCTV Cameras

Choosing the Best Installation Paths In the Development of Urban CCTV Cameras

Data-driven bottleneck detection on Tehran highways

In metropolitan areas, traffic congestion has become a prevalent challenge due to rapid urbanization and increased vehicle usage, adversely impacting mobility, productivity, and quality of life. Identifying and mitigating persistent traffic bottlenecks is crucial for developing efficient transportation systems and guiding infrastructure planning decisions. This research proposes an innovative data-driven methodology to pinpoint recurrent traffic bottlenecks in Tehran's extensive highway network, addressing the limitations of traditional traffic monitoring methods. Through data mining and image processing techniques applied to 16 months of traffic flow maps from Google Maps, diverse information is extracted, including traffic nodes, congestion hotspots, and locations with the longest queue lengths. The image processing approach involves color-based segmentation, pixel-level analysis, and machine learning algorithms to determine congestion levels across the highway network. The identified bottlenecks are validated against ground truth data from CCTV cameras, demonstrating a remarkable 92 % correlation for key identified points. The proposed approach leverages the power of advanced analytics to comprehensively analyze all major highways, including areas lacking CCTV infrastructure. The robust validation process reinforces the reliability of this data-driven solution in capturing real-world traffic dynamics. As urban mobility challenges escalate globally, the integration of diverse data sources and cutting-edge techniques will be instrumental in guiding intelligent transportation planning and policy decisions.

Intelligent Traffic Control with Convolution Neural Networks

In cities across the globe growing populations and more cars on the roads have led to worst traffic jams. This causes delays, stress higher fuel use, and more air pollution, with big cities feeling the worst effects. There's an urgent need to check road traffic density in real-time to control signals and manage traffic better. Our Smart Traffic Management System uses Convolutional Neural Networks (CNNs) and CCTV cameras to tackle this issue. This new approach allows for exact traffic density calculations, which helps adjust traffic signals based on how many vehicles are present. As a result, it eases congestion, speeds up travel, and cuts down on pollution. By using CCTV Cameras adding Convolutional Neural Networks, and tapping into the strengths of Computer Vision, our solution has a big impact on city travel. [4].

Investigating the Spatial Relation between Landuse and Property Crime in Kuching, Sarawak through Location Quotient Analysis

Urban areas are often associated with higher crime rates, which is a growing concern among communi-ties. This study aim to investigate the spatial relation between landuse and property crime in Kuching, Sarawak through location quotient analysis. There are three method were applied in this study inclu-ding multiple buffer analysis, Pearson’s correlation and location quotient. Based on initial findings, there is an observed increase in crime levels as the distance from the centroid extends from 150 to 750 meters and decrease beyond the 750-meter. The study findings reveal a strong and consistent positive correlation between property crime and land use areas across 2015-2017. Property crime is more pre-valent in urban and associated areas compared to other land use categories. Offenders in Kuching of-ten utilize various tools to break into houses and digs holes, break locks, climb gates, engage in snatch theft using motorcycles, and break car windows to steal handbags that are placed on the right seat. In certain locations, CCTV cameras are positioned far away from the target areas. All of these factors contribute to creating opportunities for offenders. Property crimes were more common during the day-time than at night as during the daytime may be attributed to more people being active outside their homes, providing criminals with easier targets. At night, people tend to stay home, the opportunity for property crimes decreases. The study provides crucial geographic crime information to the Commis-sion of Kuching North City Hall and the Council of the City of Kuching South to enhance urban safety.

Perceptions of school administrators, teachers, and students on the use of CCTV cameras and their impact on learning mathematics

Perceptions of school administrators, teachers, and students on the use of CCTV cameras and their impact on learning mathematics

Effect of Age on Sexual Behaviour, Scrotal Circumference and Scrotal Surface Temperature in Sahiwal Bulls

Age of the bull influences the sexual behaviour and quality of semen. The present study was conducted to find out the age window at which the bull shows best reproductive performance. Fifteen Sahiwal bulls were selected and classified into three age groups (n=5, in each group) - G1(2- 3.5), G2 (>3.5-8) and G3 (>8-12) years. The sexual behaviour was recorded at the time of semen collection by CCTV camera at 15 days interval. Scrotal circumference (by scrotal tape) and scrotal surface temperature (by IRT) were also taken at monthly interval. The data so obtained in the experiment was subjected to statistical analysis by one-way analysis of variance (ANOVA) using SPSS software version 26.0 to draw scientific interferences. The results revealed that the mean value of scrotal circumference was significantly (p<0.05) low in G1 as compared to the other two groups. No significant difference was observed among the groups for scrotal surface temperature and scrotal temperature gradient. Most of the sexual behaviour parameters were significantly high in the G2 (>3.5-8) group except for the time to enter arena from preparation site and total number of mountings. Therefore, it can be concluded from this study that sexual behaviour is influenced by age and adult (middle) aged bulls performed better as compared to young and old groups.

Implementation and optimisation of intelligent police systems based on artificial intelligence

The rapid development of artificial intelligence provides new opportunities for law enforcement agencies. Nowadays, the developed countries of the world are increasingly using surveillance cameras to monitor public safety, detect criminals and suspicious objects. The facial identification systems on the market have tremendous potential to help law enforcement agencies. Facial recognition software helps to identify missing persons and criminals whose faces are caught on CCTV cameras. The use of artificial intelligence in such systems accelerates their operation, which, in turn, facilitates the quick search for suspects and their rapid apprehension. Modern video surveillance systems can help counter terrorist attacks by tracking and identifying people and suspicious objects. On the other hand, the issue of personal data protection and privacy when using CCTV cameras to identify people's faces is increasingly being discussed. The obvious solution to this problem is to regulate it at the legislative level, in particular, to introduce guidelines aimed at ensuring transparency and accountability of the use of facial recognition software. For a more objective understanding of the circumstances which should be regulated by law, the author conducts a study of modern technical solutions in the field of facial identification with integrated artificial intelligence, their features and possibilities of use in the work of the National Police of Ukraine, and also identifies the steps which outline the sequence of actions during objective facial identification of people and ensure the high quality of this process and the reliability of its results.

Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3

The application of an electronic violation detection system has begun to be implemented in many countries using CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in the form of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat belts, the use of cell phones while driving, which is influenced by the number of vehicles, vehicle speed and lighting, which can increase the difficulty in the detection process. This research developed a traffic violation detection system using a hybrid model, namely the CNN and LSTM algorithms for the application of discipline using seat belts. The dataset was obtained from RoboFlow Universe with a total of 199 front view car images consists of 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while the LSTM algorithm plays a role in the prediction process. Additionally, the performance evaluation of the CNN+LSTM algorithm will be measured using the accuracy value to measure the performance of the training process and testing process. When measuring the performance of the training process, it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, Yolo3, Yolo3+LSTM. The test results show that Yolo3+LSTM has a higher accuracy compared to the others, at 89%. Next, in the testing process, the CNN+LSTM model will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models have a higher accuracy of 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85%.

Choice of architecture of video surveillance systems of State Border Guard of Ukraine based on cloud and fog technologies

Modern video surveillance systems have evolved from closed-circuit television systems to the level of complex systems that operate as part of complex integrated systems and solve the tasks of not only recording events in the sectors of view of video surveillance cameras but also analysing the received video information. The increase in the amount of information circulating in modern systems requires the creation of new concepts. In recent years, the attention of scientists has been drawn to distributed information processing technologies. The concept of cloud computing is developing rapidly, and the basic ideas that were laid down in its construction can no longer solve the problems faced by the technology. This has led to the emergence of so-called post-cloud architectures that extend and complement the capabilities of cloud computing. These architectures include Mist, Edge, Fog, etc. The purpose of the article is to study video surveillance systems based on the concept of cloud and fog computing and criteria for assessing their effectiveness. The research was conducted using the methods of analysis and generalisation, modelling, and experimentation. This approach made it possible to conduct a comparative study of video surveillance systems built on two architectures. The results obtained indicate that fog computing technology has the advantage of reducing latency, minimising the need for repeated requests to the cloud by performing calculations at the cloud edge. In a model based on the cloud computing concept, services that use cloud resources lead to an increase in network load. At the same time, fog computing technology allows you to relieve the network load by performing part of the computation by fog nodes. The results of the experimental study show the advantages of fog computing for networks that are sensitive to delays. However, if we consider a video surveillance system with the main task of recording events in the sectors of view of CCTV cameras and the ability to view video information in real time, it is obvious that fog nodes will not provide long-term storage of video information, and delays will not be critical. In the case of a video surveillance system with video analytics functions, fog nodes will be able to perform part of the video analytics algorithms, thus unloading the cloud. Therefore, an urgent task is to study the effectiveness of building video surveillance systems with video analytics functions based on fog architecture.

Pedestrian Abnormal Behavior Detection System Using Edge–Server Architecture for Large–Scale CCTVEnvironments

As the deployment of CCTV cameras for safety continues to increase, the monitoring workload has significantly exceeded the capacity of the current workforce. To overcome this problem, intelligent CCTV technologies and server-efficient deep learning analysis models are being developed. However, real-world applications exhibit performance degradation due to environmental changes and limited server processing capacity for multiple CCTVs. This study proposes a real-time pedestrian anomaly detection system with an edge–server structure that ensures efficiency and scalability. In the proposed system, the pedestrian abnormal behavior detection model analyzed by the edge uses a rule-based mechanism that can detect anomalies frequently, albeit less accurately, with high recall. The server uses a deep learning-based model with high precision because it analyzes only the sections detected by the edge. The proposed system was applied to an experimental environment using 20 video streams, 18 edge devices, and 3 servers equipped with 2 GPUs as a substitute for real CCTV. Pedestrian abnormal behavior was included in each video stream to conduct experiments in real-time processing and compare the abnormal behavior detection performance between the case with the edge and server alone and that with the edge and server in combination. Through these experiments, we verified that 20 video streams can be processed with 18 edges and 3 GPU servers, which confirms the scalability of the proposed system according to the number of events per hour and the event duration. We also demonstrate that the pedestrian anomaly detection model with the edge and server is more efficient and scalable than the models with these components alone. The linkage of the edge and server can reduce the false detection rate and provide a more accurate analysis. This research contributes to the development of control systems in urban safety and public security by proposing an efficient and scalable analysis system for large-scale CCTV environments.

A Recurrent Neural Network on Video-Based Face Identification Suspicious and Also Wanted People Recognition in ATM

The need for an intelligent recognition system is still increasing. Traditional approaches based on access to restricted places like ATM or suspected actions as theft, scam, and loitering. They are insufficient to identify suspects. These actions do not represent a real key of suspects. This project is motivated not only by the limits of the traditional approaches but also by the complexity of Intelligent Algorithms (RNN). In this project, we propose an approach for the automatic comparative labeling of facial soft biometric. The three main categories wanted persons, regular customers, and new persons serve distinct purposes. For wanted people recognition, the system is trained to identify individuals with outstanding or flagged in law enforcement databases. Regular ATM customers are recognized based on their registered facial features. New persons, on the other hand, are those not previously registered, and the system is designed to capture their facial data for future reference. Using a subset from the RNN-network datasets, recurrent neural network our experiments show the efficacy of the automatic generation of comparative facial labels, highlighting the potential extensibility of the approach to other face recognition scenarios and larger ranges of attributes. Recurrent Neural Network (RNN) system can be implement on CCTV cameras and it will be alerted particular officers. The application is easy to use and it is equipped with pyttsx3 so that the face detected is communicated to the people as voice alert.

Blockchain-based Pipeline Custody System (BPCS) for Preserving Critical Video Evidence

<p>The explosive development of network technology has driven the evolution of civilization, leading to the higher crime rate. Hence, CCTV cameras have been widely deployed to monitor criminal behaviors. The video of CCTV is considered as significant digital evidence. Due to the property of digital data, it is easily to copy and modify without electronic footprint, unlike the physical records. In case, the law enforcement starts to employ the blockchain technique due to its advantages, decentralization, transparency, and traceability, which can improve the legal effect. Unfortunately, blockchain network produces the tremendous overhead when it processes the data preservation based on the distributed communication. Thus, the gap and burden of data synchronization are the derivative issues. In order to solve the above problems, we aim to realize a blockchain-based pipeline custody system, so-called BPCS. The critical frame determination (CFD) technique is designed to catch the potential evidence image from the monitor, which is able to mitigate the storage cost of blockchain. Especially, the pipeline and directional ring strategy is proposed to aggregate the potential evidence in a batch file. This can reduce the burden on the chain of custody and shorten the time gap. The experimental results have demonstrated that BPCS can firmly possess the features of credibility and efficient through the comparisons and storage simulation.</p> <p> </p>

DESIGN AND ANALYSIS ON IMAGE COMPRESSION USING NEURAL NETWORKS

Due to the explosion of video based information proliferating in the world due to the ubiquitous usage of video cameras the amount of video based information that is currently being generated around the world is huge. And due to security purposes it is becoming imperative that these video data needs to be stored in computer memory for an extended period of time for referrals by security agencies. Because of the advancement of imaging technologies that is being used nowadays it is possible to capture extremely detailed high definition images. But it is not physically possible to store all these high-definition images in computer memories for a long time because infrastructure providers will run out of memory. Image compression is a technology which assists us in this regard. Nowadays this technology has moved from image compression to video compression to compression of 3- dimensional videos which is now becoming more and more popular due to the ever increasing usage of Augmented Reality, Virtual Reality and Ec The quantity of image data produced in modern surveillance networks is rising exponentially year on year which necessitated development of novel schemes for reducing the sizes of the images captured by CCTV cameras while not compromising on the image quality increases when the images are decompressed. This paper proposed a novel Deep Neural Network based method of compressing images in which the image accuracy is not lost but the space it occupies in the memory storage of the computes is reduced greatly compared to other image compression schemes, this proposed scheme is best suited for usage in CCTVs and other networks using Internet of Things which record images and videos continuously.

Smart Video Surveillance Using Deep Learning

Abstract: There is a lot of assessment happening in the business about video observation among them; the occupation of CCTV accounts has been blocked. CCTV cameras are put all around the spots for perception and security. In today's digital age, the increasing availability of data and advancements in computer vision have paved the way for numerous applications of object detection. This powerful technology has found its significance in various domains, including video surveillance and image retrieval systems. Object detection enables machines to identify and locate objects within images or video frames, providing valuable insights and aiding in decision-making processes. This article explores the applications, challenges, techniques, and future trends of object detection in the context of video surveillance and image retrieval systems. Object detection is a computer vision technique that involves identifying and localizing objects within images or video frames. Unlike image classification, which assigns a single label to an entire image, object detection goes a step further by detecting and delineating the individual objects present. It enables computers to comprehend visual data and interact with the world, much like humans do

Exploring the Effects of Corruption on Administration of Justice in Uganda

Corruption in Ugandan courts is still a widespread problem that must be aggressively combated before the rule of law could be upheld. It is sad to see the thriving nature of corruption in the Ugandan judiciary despite the provisions of the Anti-Corruption Act. This article explore the effects of corruption on the administration of justice in Uganda. Data for the article were secondarily sourced from journal articles, technological blogs, policy briefs, statutory provisions, case laws, newspaper publications and textbooks. At the end, the article recommends for the Inspector General of Government’s office to create an independent spy network that can discover corruption in courts. More so, the provisions of the Anti-Corruption Act should be enforced without favouritism, and the Ministry of Justice should support whistleblowers policy, who should always be on ground to reveal corruption incidences. Furthermore, CCTV cameras should be effectively and efficiently deployed in various judicial offices. Finally, justice dispensation processes should be sped up in Uganda. Keywords: Administration of justice, Anti-Corruption Act, Corruption, Court, Whistleblower.