CCTV Cameras Research Articles

Special Police Team Towards the Bright Point Brigadier Joshua's Murder Case

The obstacles faced by the police in uncovering this case are 1). Police officers have cleared the crime scene; 2). Brigadier J's HP Lost; 3). Witness Shut Up; 4). Other Obstacles regarding CCTV at the Office House Damaged; 5). Putri Candrawati is a key witness who is still alive. The bright spots for the cases that led to the death of Brigadier J include 1. The presence of Tita President Joko Widodo; 2. There is suspicion of premeditated murder; 3. It has been obtained and turned on CCTV cameras around the crime scene that are lost or dead; 4. Brigadier J's cellphone and CCTV have been examined in a forensic laboratory based on the Scientific Crime Investigation processed by a forensic laboratory; 5. There is a re-autopsy. To open the veil of Brigadier J's case based on President Joko Widodo's order, the National Police Chief finally named a suspect in the shooting case that killed Brigadier Yoshua Hutabarat, based on 1). Initial evidence is sufficient; 2). Other possible suspects; 3). The threat of punishment (Bharada E; 4). Bharata E was arrested; 5). Not self-defense; 6). Murder suspect; 7). Initial chronology.

A Sociological Inquiry on Training and Treatment of the Wellbeing of Female Inmates in Correctional Facilities in Ogun and Lagos States, Nigeria

All over the world, the UNODC policy of 2011 agitates for needs to train, treat and promote the wellbeing of female inmates so as to reduce and control rate of recidivism in the country. Women are considered as care givers whose duty includes socialisation; domestication and procreation of offspring for societal continuity. In Nigeria, once women are convicted or apprehended by law, they faces lot of challenges ranging from stigmatization, poor health treatment, isolation, psychological trauma, unequal treatment among others Relevance publications to include local and international journals, magazines, newspaper, textbooks and articles were reviewed and double deviance feminist theory was adopted for justification of the inquiry. A mixed method was adopted with cross sectional survey method. Total population for the study in the three selected correctional facilities in Lagos and Ogun states was estimated to 348 inmates but 250 was found useful for the quantitative analysis while 15 female inmates were selected for the qualitative analysis. The result indicated that there are many factors that increase female involvement in criminal acts ranging from greed, poverty, unemployment. The second result shows that the treatment available for female inmates has insignificant negative effect on their wellbeing while the last indicated that training have significant relationship with female wellbeing in the facilities. The government, non-governmental organisation and the philanthropists should collective support the promotion of the wellbeing of female inmates and adequate cares and supervision should be ensure through the use of CCTV cameras in the facilities

Deep learning features in facial identification and the likelihood ratio bound

In recent years, the score-based likelihood ratio (SLR) method for facial comparison has attracted considerable research attention. This method relies on the match scores that are calculated from the features obtained from facial recognition systems, deep learning based in particular. However, this concept has not been completely understood. Therefore, this study is aimed at investigating deep learning facial features, and the SLR levels of their match scores. We propose a new interpretation that the deep learning feature is a class characteristic. Based on a large-scale data set experiment, we present evidence that the log SLR value of deep learning features can reach 8 in some data sets. The study results imply that the SLR of deep learning features is a useful method for facial identification, especially when the suspected image is obtained via a CCTV camera.

Real-Time Human Motion Tracking by Tello EDU Drone

Human movement tracking is useful in a variety of areas, such as search-and-rescue activities. CCTV and IP cameras are popular as front-end sensors for tracking human motion; however, they are stationary and have limited applicability in hard-to-reach places, such as those where disasters have occurred. Using a drone to discover a person is challenging and requires an innovative approach. In this paper, we aim to present the design and implementation of a human motion tracking method using a Tello EDU drone. The design methodology is carried out in four steps: (1) control panel design; (2) human motion tracking algorithm; (3) notification systems; and (4) communication and distance extension. Intensive experimental results show that the drone implemented by the proposed algorithm performs well in tracking a human at a distance of 2-10 m moving at a speed of 2 m/s. In an experimental field of the size 95×35m2, the drone tracked human motion throughout a whole day, with the best tracking results observed in the morning. The drone was controlled from a laptop using a Wi-Fi router with a maximum horizontal tracking distance of 84.30 m and maximum vertical distance of 13.40 m. The experiment showed an accuracy rate for human movement detection between 96.67 and 100%.

Forensic Accounting Techniques on Fraud Prevention in Tertiary Institutions in Cross River State, Nigeria

This study examined the influence of forensic accounting techniques on fraud prevention in tertiary institutions in Cross River State, Nigeria. The study's methodology was a survey research design. Two research questions and two null hypotheses were developed in order to fulfill the study's objectives. Due to the manageable size of the population, all 152 auditors in bursary department participated in the study. A questionnaire titled “Forensic Accounting Techniques on Fraud Prevention” (FATFP) with a four-point rating scale was used for data collection. Two specialists in accounting department and one expert in measurement and evaluation validated the instrument. The Cronbach's alpha method was used to calculate the overall reliability coefficient of the instrument, which is.79. The standard deviation and mean were used to answer the research questions. The null hypotheses were tested using an independent t-test at the.05 level of significance. The findings revealed that computer-assisted audit techniques and interview audit techniques had a significant influence on fraud prevention at tertiary institutions. Among other things, it was recommended that, government should arm auditors with information and communication tools to aid auditing procedures for fraud prevention. Once again, auditors should use contemporary interview approaches, including watching candidates during interviews, using recording equipment, and installing CCTV cameras strategically placed to capture correct data that will aid in stopping fraudulent operations in Nigerian educational institutions, particularly in Cross River State, Nigeria.
 
 Received: 11 November 2022 / Accepted: 28 December 2022 / Published: 5 January 2023

IoT BASED WHEELCHAIR FALL DETECTION SYSTEM

It is important to take care of our elderly people and keep an eye on their well-being and security. It is likely that they have a high risk of falling because of their fragile bones and joints, which brings in the usage of wheelchair for most of the old people. A wheelchair-bound individual who is physically impaired needs to be watched out for falls as well. There are currently monitoring systems using CCTV cameras, but they are very expensive. The average person cannot afford such a system. As a result, we suggest an affordable fall detection system using the newest technology such as Internet of Things (IoT). The system uses gyroscope and accelerometer sensors to detect individual movements. Both collaborate to detect the fall events improving the detection’s accuracy. It might be mounted on a person's hand or wheelchair for finding. The sensor is connected to a microcontroller so that it can continuously monitor the data about the increasing speed. What if the individual did not fall and the warning given was false? The framework allows the alarm to be turned off if the individual presses the snooze button within 5 seconds. Therefore, IoT will reduce user effort while also generating fertile work that makes accessing the system simple. Keywords- Arduino UNO R3, w h e e l c h a i r f a l l d e t e c t i o n , a c c u r a c y , S e n s o r s

Study on debris flow monitoring in Mt. Fuji

The Osawa River, which has its headwaters at the top of Mt. Fuji, has one of the largest failure in Japan, the Osawa Failure, in its uppermost part of the catchment. The Osawa Failure produces more than 100,000m3 of sediment every year by erosion and collapse, which causes debris flows and slash avalanches. Since its opening in 1970, the Fuji Sabo Office has been observing the sediment dynamics of the Osawa River through field surveys, observation equipment and aerial photogrammetry, aerial laser survey, and Japanese pipe hydrophone. In 2021, the largest debris flows since the start of observations in the Osawa River occurred twice in March and August. In the March 21 debris flow, 416,000m3 of sediment flowed down from the Osawa Failure, and 482,000m3 was deposited in the alluvial fan. In the August 18 debris flow, 421,000m3 of sediment was deposited in the alluvial fan. Our observations from CCTV cameras, rain gauges, and other equipment installed in the Osawa River indicate that the rainfall characteristics during the two debris flows were different, and that the conditions for debris flows differed depending on the season.

Curbing Traffic Rule Violation Using Machine Vision

According to a 2017 report of the Bureau of Police Research there are 20 crore registered vehicles for 72,000 traffic police. Though the number of traffic police have not changed much the number of vehicles has gone up to nearly 30 crore in 2022. Many drivers know that the probability of getting caught is very less. So, traffic rules like speed suggestions are taken more as a suggestion than a rule to be followed. This leads to many road accidents. The National Crime Record Bureau data on the accidental deaths in India shows the number has increased from 3,54,796 in 2020 to 4,03,116 in 2021.the fatalities have increased by 16.8 percent. The Cause-wise analysis reveals that most of the road accidents were due to over speeding accounting for 2,40,828 out of 4,03,116 cases (59.7%) causing 87,050 deaths and injuring 2,28,274 people. This project aims to reduce these over speeding violations by processing the CCTV camera footage to identify the violators and warn them and to fine the repeat violators. With the ever-increasing number CCTV cameras being installed across India and knowing that they are constantly monitored by the authorities will push the drivers to follow the rules more strictly

A Novel Front Door Security (FDS) Algorithm Using GoogleNet-BiLSTM Hybridization

Security has always been a significant concern since the dawn of human civilization. That is why we build houses to keep ourselves and our belongings safe. And we do not hesitate to spend a lot on front-door locks and install CCTV cameras to monitor security threats. This paper presents an innovative automatic Front Door Security (FDS) algorithm that uses Human Activity Recognition (HAR) to detect four different security threats at the front door from a real-time video feed with 73.18% accuracy. The activities are recognized using an innovative combination of GoogleNet-BiLSTM hybrid network. This network receives the video feed from the CCTV camera and classifies the activities. The proposed algorithm uses this classification to alert any attempts to break the door by kicking, punching, or hitting. Furthermore, the proposed FDS algorithm is effective in detecting gun violence at the front door, which further strengthens security. This Human Activity Recognition (HAR)-based novel FDS algorithm demonstrates the potential of ensuring better safety with 71.49% precision, 68.2% recall, and an F1-score of 0.65.

Enhancing safety measures at the Kintampo Waterfalls in Ghana: A post-disaster evaluation

The study was conducted to evaluate the safety measures put in place at Kintampo Waterfalls in Ghana following the March 18,2017, natural disaster,which led to the death of eighteen (18) students,with three (3) others sustaining varying degrees of injury. The study used a case study design. Through the observation methodand interview, data was gathered to assess the safety and security measures at Kintampo Waterfalls. The study revealed that safety signs, a first aid box, a fire extinguisher, a washroom, dustbins, tour guides, security personnel, visitors seating, and an emergency unit were available at the site. However, CCTV cameras, metal detectors, emergency response teams, and ambulances were not available. The study findings imply that destination management has attempted to improve safety and security practices at the site after the incident to avert any future occurrences. The study recommends that destination management should endeavor to provide CCTV cameras, metal detectors, permanent ambulanceservice, permanent police personnel, and an emergency response team to deal with any situation that may arise at the destination. The findings of the study provide current data on the state of Kintampo Waterfalls after the disaster to policymakers, destination managers, and the general public.

The Contribution of Science and Technology in Countering Violent

This study examines the role of science and technology in countering violent extremism (CVE) in Lamu County, Kenya. The study employed a descriptive research design and mixed-method cross-sectional survey approach, focusing on multi-agency team operations, specifically emphasizing Kenya Defence Forces (KDF) activities in the Boni Forest. The research population comprised key stakeholders and practitioners involved in CVE efforts, totaling 120 officers. A purposive sampling technique ensured diversity within the sample based on participants’ expertise, background, functional areas, departments, age, and units within the Kenya Defence Forces. Data collection involved structured questionnaires and key informant interviews for primary and secondary data from books, journals, and operating procedures. Quantitative data analysis employed SPSS for descriptive statistics, while thematic analysis was applied to qualitative data. The study reveals that science and technology have been integral to Kenya’s CVE efforts, with the National Counter-Terrorism Center (NCTC) playing a significant role in intelligence gathering and analysis using technology. Collaboration with international agencies allowed sharing of intelligence, aiding the prediction of attacks and identification of areas with prevalent radicalization. Surveillance technologies like drones and CCTV cameras have enhanced monitoring in high-risk areas, leading to effective response measures. Moreover, mobile money transfer systems like M-Pesa are beneficial and susceptible to misuse in financing terrorist activities, emphasizing the importance of partnerships between financial institutions and security agencies. The study recommended integrating science and technology with community engagement, education, and policy initiatives. It underscores the need for a comprehensive community-centered strategy in Lamu County, fostering partnership and trust between the government and local communities. Continuous research, capacity building, and counter-narratives using technology are essential for sustained progress in countering violent extremism. While science and technology offer invaluable tools, collaboration, and holistic approaches remain vital to achieving comprehensive results and maintaining national security.

Validation of a Driving Simulator for Road Tunnel Behavioural Studies

Introduction. According to European regulations, road tunnel safety is strategic in the management of national and international road corridors. Although the accident rate is lower in tunnels than on open roads, the severity of crashes in tunnels is higher due to the presence of hard lateral obstacles and limited space in case of lane departure. Driving simulation studies can support design decisions to assess the impact of any safety improvement albeit driving simulators must be validated to understand how the experimental results relate to real driving conditions. Method. This study deals with the behavioural validation of the fixed base driving simulator of the RSDS Lab for safety studies for tunnels. Field speed and lateral position data for vehicles were collected by image analysis of video sequences collected from the CCTV cameras in five sections of the Fréjus tunnel (Italy-France). The tunnel was faithfully modelled in the virtual scenario, and the same data were collected by extracting records at the same cameras’ stations. Thirty-five participants were involved in a between-subject experiment. Fifteen drivers with Italian B licenses drove a car, and twenty professional drivers with Italian C and/or D licenses drove a heavy truck. Results and Conclusions. Normality tests for data distributions and t-tests for the comparison between real and simulated data were conducted. The simulator achieved the relative validation for truck speeds (with values observed in the simulation always lower than those observed in real driving), and absolute validation with regard to truck lateral position. Opposite outcomes were obtained for cars, with absolute validity for speed and relative validity for lateral position. The relative-absolute validation of the driving simulator enables us to establish how experimental outcomes can be generalized to understand the impact of any safety countermeasure.

Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse, when patient emotion is fear, cry or sad. The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm. The proposed Convolution Neural Networks (CNN) architecture with DnCNN preprocessing enhances the performance of recognition. The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras. The proposed method accuracy is more when compared to other methods. It detects the chances of suicide attempt based on stress level and emotional recognition.

INTEGRATION OF SENSOR TECHNOLOGIES IN TRANSPORT SYSTEMS TO CREATE SUSTAINABLE INTELLIGENT TRANSPORT SYSTEMS

Every day on the road, we face the problems of traffic jams, traffic safety and environmental pollution. All this is directly related to the quality and efficiency of transport systems. To solve these problems, information and communication technologies are used, which are increasingly integrated into modern transport systems. Car manufacturers are developing automotive sensors to improve vehicle safety, traffic management and to develop infotainment systems. Government agencies, in turn, are deploying roadside infrastructure, installing CCTV cameras and road sensors to collect environmental and traffic data. Thanks to the seamless integration of sensor technologies into vehicles, their sensor and communication capabilities can be used to create intelligent transport systems. The article discusses the prospects of using sensor sensors to ensure the efficiency of the functioning of transport systems and transport infrastructure as a whole. An analysis of the types of sensors was carried out depending on the object of use - for vehicles and for roads. The programs to ensure the operation of certain sensors are considered separately by category. The process of integration and operation of sensor technologies with information and communication technologies to ensure the effective functioning of the intelligent transport system is considered as an example. Having drawn a conclusion from the conducted research, it can be assumed that the development of sensor technologies and their integration into the transport system will significantly increase the safety of traffic participants and increase the efficiency of the transport system as a whole.

Development of optimal and cost effective bus scheduling using genetics algorithm

Most higher institutions in Nigeria operates intra and inter campus transportation, but lack proper planning on movement schedule which contributes significantly to poor academic performance of students as it causes great fatigue due to long queue at the parks and consequently, resulting to losses of revenue to bus management. A met-heuristic algorithm, G.A and L.P model were used to optimize bus scheduling for efficient transportation. To achieve this, travel demands for peak and off-peak seasons at the two campuses of FUTMinna, Nigeria were obtained using 4 numbers of CCTV cameras located at strategic positions. Data were analyzed using the design travel times of 40, 50 and 60 minutes considering traffic and road conditions using 19 numbers of 18 seater bus, 11 numbers of 35 seater bus and 15 numbers of 60seater bus capacities buses with15 minutes departure time. It was observed that with N100/head of revenue charges, a total of N185,000/day corresponding to 231 trips would be achieved during off-peak season to convey 2,562 students, and this amount would increase by 289% at peak season with very little or no delay to move 4,411students travel demand.

The Use of CCTV Model Analysis for a Smart Environment in Semarang City

This study examined the concept of a smart city and the importance of a smart environment in city management. CCTV analytics has been employed to monitor and manage urban environments, especially in Semarang City. Smart city and CCTV analytics challenges facing the Semarang City Government were investigated. Interviews, document analysis, and field observation provided input for this case study, combined with specific cases to gather detailed explanations from various sources or stakeholders. The installation of a CCTV analytics system in Semarang aims to create a smart environment. The city strategically installed CCTV cameras with analytical technology and a data center to process and store CCTV data. This system monitored high-risk areas such as flood plains. The Quick Response Team handled problems immediately after the early warning system was activated. However, funding, analytical CCTV supply, and system integration were issues. Hence, the government must address these challenges to manage the environment and reduce risks. This research discovered that analytical CCTV in Semarang City provided real-time data and visualization of environmental conditions, helping the government make the right decisions and policies. Access to CCTV footage and decision-making benefited the community. Air pollution and flooding must be addressed as the integration continued. This study emphasizes urban environmental management strategies and the pros and cons of technology integration. The potential of smart environments and the challenges of smart city implementation require more research and development.

DynaTrack: Machine Learning Aided Variable Speed Limit System

In the past decade or so, we have seen traffic congestion become a ginormous issue in the major cities of the world, the main reasons for the above being exponential growth in vehicle purchases, constant unplanned maintenance of the road networks, and so on. And as far as the current situation is concerned, it can be safely said that even though steps have been taken to counter the issue at hand, the improvement made has been close to nothing as the problem persists. The solution to this is being suggested is based on the concept of Variable Speed Limits, which says that the speed limits for the road segments be dependent on the traffic volume and situations on the road segment rather than being constant. The system would use the feed from CCTV cameras to keep a track of the flow variable, which lies within the low traffic limits, the speed limits would be increased accordingly. This would ensure convenience for travelers, reduce travel time, fuel consumption, and greenhouse gas emission, and will also improve economic gains, among other advantages.

Machine learning and IoT-based garbage detection system for smart cities

Today, detecting waste, collecting it, processing it, and getting rid of it are among the most significant environmental issues in developing and undeveloped counties. It has been observed that a large amount of garbage remains strewn on the roadside. This study presented a garbage detection technology such as machine learning and gadgets connected to the Internet of Things (IoT), such as an IP-enabled CCTV camera, to take pictures and send them to the city’s main server. The input images are transformed into a two-dimension array of integers using Python modules and divided into the garbage and no garbage classes. There is an 80:20 split between the training and testing datasets from the input dataset. Preprocessed images are then utilised as inputs for a wide range of machine learning and neural network models for classification; these include K-Nearest Neighbour (KNN), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM). The test data sets are applied, and a confusion matrix is formed for all models to analyse the efficiency and performance of the trained models. Results from the confusion matrix are contrasted with those from the area under the Receiver characteristics operating curve (AUC). As a result, the ConvNet model is best suited for classifying garbage or no garbage present in open space, and the LR model proposed best suits the garbage detection problem. The proposed models are best suitable for improving the efficiency of existing garbage identification systems and developing a new system for smart cities.

Security System Uses CCTV Camera as Facial Image Recognition using Face-API

Security System Uses CCTV Camera as Facial Image Recognition using Face-API

TEACHERS' VIEWS AND OPINIONS ABOUT THE STRATEGIES TO MITIGATE THE MALPRACTICES IN SECONDARY SCHOOL CERTIFICATE EXAMINATIONS SYSTEM IN BOARDS OF KHYBER PAKHTUNKHWA

This study is being conducted to "investigate the views and opinions of teachers about the strategies to mitigate the malpractices in the secondary school certificate examination system in the boards of Khyber Pakhtunkhwa." The target population was all the teachers of secondary schools in Khyber Pakhtunkhwa, a province of Pakistan. The data was collected through questionnaires, and the following five hypotheses were tested: (1) The installation of CCTV cameras in examination halls will minimize malpractices during examinations. (2) The appointment of good-character, dutiful, and honest teachers as exam supervisors will minimize the chances of malpractice during examinations. (3) The media can play a positive role in reducing examination malpractices. (4) Good and effective teaching in schools will minimize the chances of malpractices in examinations. (5) Strict punishment for individuals involved in malpractices will reduce the occurrences of examination malpractices. The data was tabled, analyzed, and a conclusion drawn. All the hypotheses were proven true and accepted. Keywords: Teachers, malpractice, CCTV cameras, hypotheses, Khyber Pakhtunkhwa