Closed-circuit Television Cameras Research Articles

Efficient Method of Road Outlier Recognition Using Deep Learning Coupled with Data Augmentation Approach

In recent years, Closed-circuit Television (CCTV) cameras have been playing a vital role in the surveillance of both public and private areas. The primary objective of surveillance is to monitor human behavior and road conditions. In the real-world situation, detecting, and recognizing abnormal activities poses significant challenges due to the densely crowded environment and the complex nature of transportation systems. These factors make it difficult to automatically identify various anomalies that occur while traveling, leading to emergencies, and endangering human life and property. In response to these challenges, contemporary Road Outlier Recognition Surveillance Systems (RORSS) have been deployed to monitor and mitigate abnormalities on streets, highways, and roads. This research specifically investigated conventional as well as human-centered road anomalies encompassing snatching, accidents, fighting, and car fires. The proposed Real-time Road Outlier Recognition methodology is formulated as a classification task, involving the analysis and processing of real-time CCTV videos. The proposed study investigated different types of Convolutional Neural Network (CNN) pre-trained models with the Data Augmentation (DA) approach to address the frame variance problem in real-time videos. Furthermore, we introduced a benchmark real-world Road Outlier Dataset (ROD) containing roads, streets, and highways videos and images that presented different road anomalies performed by vehicles and humans. Experimentation using different pre-trained CNN models i.e., VGG19, InceptionV3, ResNet50, and DenseNet201 with a data augmentation approach has been performed on the ROD dataset. The experimental results demonstrated that the InceptionV3 model performed best with the augmentation approach as compared to other deep learning models that achieved the best accuracy of 98.80% in recognizing road anomalies

UNCANNY ACTIVITY DETECTION USING CCTV MONITORING

"Uncanny Activity Detection Using CCTV Monitoring" introduces an innovative approach to support public safety through the integration of Machine Learning (ML) in real-time monitoring. In the contemporary world, Closed-Circuit Television (CCTV) surveillance stands as a fundamental and highly effective security measure for various premises, including hospitals, malls, and universities. It serves as a widely recognized tool for preventing and detecting unwanted activities. However, envisioning an public space equipped with several CCTV cameras across multiple buildings presents a logistical challenge. The manual monitoring of events across this expansive network is practically impossible. Furthermore, searching for a specific event in recorded video footage, even after it has occurred, proves to be a time-consuming endeavor. This project addresses the need for an efficient solution to manage and analyze extensive CCTV footage in complex environments, optimizing security practices and response times. Keywords: Anomaly Detection, Deep Learning, Human Behavior Recognition.

Vision based intelligent traffic light management system using Faster R‐CNN

AbstractTransportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework.

Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains.

CCTV cameras at home: Temporality experience of surveillance technology in family life

Political and socioeconomic factors contribute to the popularity of surveillance camera use at home in China. This study investigates how CCTV (closed-circuit television) technology shapes family communication practices. Drawing on interviews and observations of 12 migrant parents, four left-behind children and four proximal caregivers, this article explores why people use new surveillance technology in the private home sphere to help parenting, and how family members cope with such surveillance. Children’s coping tactics include participation, disregard, escape and resistance. Through the temporalities framework, this article uncovers nuanced immediate, archival and predictive time experiences. The temporality experience of CCTV use is not traditional linear time co-presence, but video-based concentric three-circle time experience. It shifts interpersonal trust in family life into surveillance trust and interpersonal distrust, which requires familial negotiation and trust reconstruction.

On the performance of streamflow gauging using CCTV-integrated LSPIV in diverse hydro-environmental conditions.

Addressing the critical need for precise streamflow measurements in hydro-environmental research, this study evaluates large-scale particle image velocimetry (LSPIV) using cost-effective closed-circuit television (CCTV) cameras, providing a detailed sensitivity analysis in both laboratory and real-world canal settings. In laboratory conditions, a 45° camera angle notably enhanced performance, exhibiting a 12% decrease in MAE and a remarkable 40% reduction in RMSE compared to the performance of orthographic form. Tracer particles further enhanced LSPIV accuracy, decreasing both mean absolute error (MAE) and root mean square error (RMSE) by around 0.05 m/s. Optimal velocity coefficients for the lab ranged between 0.85 and 0.90. Nighttime measurements, using projection-based illumination, showed a minor 3% MAE variation and 0.02 RMSE difference versus daytime. In field experiments, a 45° upstream CCTV camera configuration notably improved LSPIV accuracy, achieving a 3% MAE and 0.055 m/s RMSE. For best results across different turbidity levels, we recommend a velocity coefficient range of 0.84 to 0.88. This study highlights the robustness and cost-efficiency of LSPIV as a transformative method for streamflow gauging, demonstrating its wide applicability in diverse hydro-environmental scenarios.

Attitudes among the Australian public toward AI and CCTV in suicide prevention research: A mixed methods study.

Research is underway exploring the use of closed-circuit television (CCTV) cameras and artificial intelligence (AI) for suicide prevention research in public locations where suicides occur. Given the sensitive nature and potential implications of this research, this study explored ethical concerns the public may have about research of this nature. Developed based on the principle of respect, a survey was administered to a representative sample of 1,096 Australians to understand perspectives on the research. The sample was aged 18 and older, 53% female, and 9% ethnic minority. Following an explanatory mixed methods approach, interviews and a focus group were conducted with people with a lived experience of suicide and first responders to contextualize the findings. There were broad levels of acceptance among the Australian public. Younger respondents, females, and those declining to state their ethnicity had lower levels of acceptance of CCTV research using AI for suicide prevention. Those with lived experience of suicide had higher acceptance. Qualitative data indicated concern regarding racial bias in AI and police response to suicidal crises and the need for lived experience involvement in the development and implementation of any resulting interventions. Broad public acceptance of the research aligns with the principle of respect for persons. Beneficence emerged in the context of findings emphasizing the importance of meaningfully including people with lived experience in the development and implementation of interventions resulting from this research, while justice emerged in themes expressing concerns about racial bias in AI and police response to mental health crises. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Development and Deployment of a Virtual Water Gauge System Utilizing the ResNet-50 Convolutional Neural Network for Real-Time River Water Level Monitoring: A Case Study of the Keelung River in Taiwan

Climate change has exacerbated severe rainfall events, leading to rapid and unpredictable fluctuations in river water levels. This environment necessitates the development of real-time, automated systems for water level detection. Due to degradation, traditional methods relying on physical river gauges are becoming progressively unreliable. This paper presents an innovative methodology that leverages ResNet-50, a Convolutional Neural Network (CNN) model, to identify distinct water level features in Closed-Circuit Television (CCTV) river imagery of the Chengmei Bridge on the Keelung River in Neihu District, Taiwan, under various weather conditions. This methodology creates a virtual water gauge system for the precise and timely detection of water levels, thereby eliminating the need for dependable physical gauges. Our study utilized image data from 1 March 2022 to 28 February 2023. This river, crucial to the ecosystems and economies of numerous cities, could instigate a range of consequences due to rapid increases in water levels. The proposed system integrates grid-based methods with infrastructure like CCTV cameras and Raspberry Pi devices for data processing. This integration facilitates real-time water level monitoring, even without physical gauges, thus reducing deployment costs. Preliminary results indicate an accuracy range of 83.6% to 96%, with clear days providing the highest accuracy and heavy rainfall the lowest. Future work will refine the model to boost accuracy during rainy conditions. This research introduces a promising real-time river water level monitoring solution, significantly contributing to flood control and disaster management strategies.

Implementation of the Law Regarding the Use of CCTV Cameras as Electronic Traffic Enforcement Tools in the City of Medan

<p>Closed Circuit Television (CCTV) is one of the surveillance tools that can work automatically 24 (twenty-four) which can be used as a tool that helps prevent crime on an object. This research uses a normative juridical approach method. The type of data in this study uses secondary data, and an empirical approach with a qualitative approach. The data sources used are documents and books. Secondary data is data obtained indirectly through literature studies of laws and regulations and publications of previous studies to add information, obtain a theoretical basis and legal basis. Data analysis is described qualitatively, by describing data in the form of sentences that are organized, logical, effective, so that it helps ease of understanding. This research results in the existence of CCTV (Closed Circuit Television) recording evidence recognized as valid evidence, reinforced by the issuance of the Electronic Information and Transactions Law, namely in Article 5 paragraph (1) and paragraph (2) of the ITE Law.</p>

FRAMEWORK FOR DESIGN OF SECURITY SYSTEMS FOR MONITORING EXAMINEES AND PROCTORS DURING EXTERNAL OFFLINE EXAMINATIONS IN NIGERIA

This paper seeks to design a system for monitoring the compliance of external examination centers to set rules and regulations governing the conduct of an external offline examination. The system is designed to check impersonation of examinees, aiding and abetting by teachers and other proctors, importing and submitting answer scripts written outside the approved examination halls and finally monitoring the script submission process to prevent swapping of answer booklets after completion of the examination. The proposed system consist of biometric authentication system, automated attendance system, Closed-Circuit Television (CCTV) cameras for video recording, a script submission system (SSS), a drop box for temporal storage of scripts and human monitors to analyse the recorded videos at monitoring center of the examination board. The system is designed to work even in remote areas with unstable internet as is the case with most examination centers in rural areas in Nigeria. When implemented the system will help check illegal activities in miracle examination centers and reduce examination malpractices by both examinees and proctors drastically.

Design of a real-time crime monitoring system using deep learning techniques

Criminal Activities and Crime Monitoring System has long been a research topic. In this paper, we propose an effective Crime Monitoring System (CMS) that can detect a crime in real-time using a camera surveillance system and notify the appropriate law enforcement officer. The CMS was proposed to counterbalance human weaknesses such as inattention, slow reaction, and slacking, for example, in detecting crimes. The proposed CMS detects crime scenes by combining the mechanisms and functionalities of closed-circuit television (CCTV) cameras with various deep-learning methods and image-processing techniques. The CMS operates in three stages: weapon detection, violence detection, and face recognition. We used transfer learning models to detect weapons, and violence and used a face recognition algorithm to recognize faces. More specifically, to detect weapons YOLOv5, and MobileNetv2 to detect violence and used face recognition algorithms to recognize faces. The image dataset is used in CMS, while the video dataset is used to train the MobileNet-based violence detection model. In this case, frame-by-frame images extracted from video files were used to train the model. All of the models performed admirably. The weapon detection model detected four different weapon classes with greater than 80% accuracy. The violence detection model is also 95% accurate. The face recognition model had a 97% accuracy rate in detecting faces. The CMS's combined model was tested in a variety of real-world scenarios, and its performance was found to be outstanding. It was able to detect crime incidents and generate timely alarms, demonstrating its effectiveness in providing security and safety.

PERAN CLOSED CIRCUIT TELEVISION (CCTV) DALAM MENINGKATKAN KEAMANAN DI KAMPUS STIE GANESHA

Closed Circuit Television (CCTV) is an essential tool for guarding and monitoring locations. CCTV is an additional tool for security, and is widely used in shops, offices, and even housing. CCTV on campus is also used to prevent, monitor, and improve the performance of lecturers, students and employees. This research aims to explore and reveal the utilization of this media in depth. The research was conducted for 2 months from June to July 2023. This research methodology adopts a qualitative descriptive approach. Data were obtained through interviews, observations, and literature studies. The findings of this study indicate that the installation of CCTV cameras on the STIE Ganesha campus is effective in carrying out three main functions: preventing, monitoring, and improving the performance of lecturers and students. The installation of CCTV plays a role in supporting lecturers in improving the quality of teaching. The implications of this research provide encouragement to continue exploring innovative ways of utilizing technology to support and enrich the educational experience on campus.

AI-Powered Intelligent Seaport Mobility: Enhancing Container Drayage Efficiency through Computer Vision and Deep Learning

The rapid urbanization phenomenon has introduced multifaceted challenges across various domains, including housing, transportation, education, health, and the economy. This necessitates a significant transformation of seaport operations in order to optimize smart mobility and facilitate the evolution of intelligent cities. This conceptual paper presents a novel mathematical framework rooted in deep learning techniques. Our innovative model accurately identifies parking spaces and lanes in seaport environments based on crane positions, utilizing live Closed-Circuit Television (CCTV) camera data for real-time monitoring and efficient parking space allocation. Through a comprehensive literature review, we explore the advantages of merging artificial intelligence (AI) and computer vision (CV) technologies in parking facility management. Our framework focuses on enhancing container drayage efficiency within seaports, emphasizing improved traffic management, optimizing parking space allocation, and streamlining container movement. The insights from our study provide a foundation that could have potential implications for real-world applications. By integrating cutting-edge technologies, our proposed framework not only enhances the efficiency of seaport operations, but also lays the foundation for sustainable and intelligent seaport systems. It signifies a significant leap toward the realization of intelligent seaport operations, contributing profoundly to the advancement of urban logistics and transportation networks. Future research endeavors will concentrate on the practical implementation and validation of this pioneering mathematical framework in real-world seaport environments. Additionally, our work emphasizes the crucial need to explore further applications of AI and CV technologies in seaport logistics, adapting the framework to address the evolving urbanization and transportation challenges. These efforts will foster continuous advancements in the field, shaping the future of intelligent seaport operations.

The Detection System for a Danger State of a Collision between Construction Equipment and Workers Using Fixed CCTV on Construction Sites.

According to data from the Ministry of Employment and Labor in Korea, a significant portion of fatal accidents on construction sites occur due to collisions between construction workers and equipment, with many of these collisions being attributed to worker negligence. This study introduces a method for accurately localizing construction equipment and workers on-site, delineating areas prone to collisions as 'a danger area of a collision', and defining collision risk states. Utilizing advanced deep learning models which specialize in object detection, video footage obtained from strategically placed closed-circuit television (CCTV) cameras across the construction site is analyzed. The positions of each detected object are determined using transformation or homography matrices representing the conversion relationship between a sufficiently flat reference plane and image coordinates. Additionally, 'a danger area of a collision' is proposed for evaluating equipment collision risk based on the moving equipment's speed, and the validity of this area is verified. Through this, the paper presents a system designed to preemptively identify potential collision risks, particularly when workers are located within the 'danger area of a collision', thereby mitigating accident risks on construction sites.

Resting pattern and preferences when lying down at night in a semi-captive population of African elephants

Despite recent improvements in semi-captive African elephant habitats and husbandry, night-time behaviour patterns are still not well documented in this species. Hence, this study investigated resting behaviour when lying down, using data recorded nine African elephants maintained in semi-captivity in South Africa. A total of 111 nights were recorded over a 33-month period (2015–2018). Closed circuit television cameras were used to record the elephant’s behaviour from 17:00–07:00. Three elephants were individually penned in a boma at night while the rest of the herd could move freely between the boma and an outdoor camp. Time laying down and standing up, location, resting side, head direction, sawdust quality, weather conditions, and number of elephants present in the boma were recorded. Data were analysed using linear mixed model and Chi-square test procedures. On average ( ± SE), the elephants slept laying down for 65.64 ± 0.79 mins (1–267 mins). They laid down 3.22 ± 0.05 times per night (0−8), with an average bout length of 69.79 ± 2.65 min and switching side at a rate of 51.03%. No difference in resting duration when laying down was observed between sex, age category, resting side, head direction, whether they were penned or not, and location (P > 0.05). Elephants rested longer laying down between 01:00 and 04:00 (P < 0.0001), with a peak between 01:00 and 02:00. They also rested longer when the weather was clear as compared to overcast and rainy conditions (P < 0.0001). An intermediate freshness of sawdust also resulted in longer resting duration (P < 0.001). Furthermore, elephants laid down more when they were unpenned (P < 0.05); when the sawdust was at an intermediate freshness (P < 0.001); when the number of elephants in the boma was at an intermediate number of 4 (P < 0.001); when the weather was overcast (P < 0.001), and when the ambient temperature was mild (P < 0.001). They were observed laying down more often between 23:00 and 03:00 (P < 0.001), with mature adults preferring the areas opposite to the main entrance of the boma while the younger elephants preferred areas closer to the main entrance (P < 0.0001). Finally, high individual variations were observed in resting duration, number of bouts and average bout length. Further studies are therefore needed to investigate how preferences, personality traits, life-history and external factors associated with eco-tourism can affect the quality of resting laying down in captive African elephants.

The Performance of K-Nearest Neighbor (KNN) Approach for Estimating Extreme Rain Events based on CCTV Images Camera Data

Abstract—Hydrometeorological disasters such as flooding in urban areas are a big problem that must be managed. Indonesia as part of the maritime continent, has high rainfall variability, both temporally and spatially. Unfortunately, the density of instruments for measuring rainfall is still low. To solve the problem, this research will try to utilize and modify Closed Circuit Television (CCTV) cameras which have a large number in terms of quantity as instruments for measuring rainfall. The purpose of this research is to obtain rainfall image information and data generated by CCTV cameras. The image data is converted to quantitative rainfall data. The method used is the K-NN algorithm and machine learning. The research location is located in a corner of the city of Bandung with a geographical position of 60 53”30.49'S and 107.035” 12.27' E. The results of this research show that the K-NN algorithm can be applied to estimate rainfall data from CCTV images with an accuracy of more than 98%. The level of accuracy generated between CCTV camera image data and AWS is 94%. The level of accuracy is high means that CCTV camera image data can represent or be converted into quantitative rainfall data.

Electronic Invigilation Inclusion in Curbing Examination Malpractices Among Postgraduate Students in Selected Public Tertiary Institutions In Rivers State

The study investigated electronic invigilation inclusion in curbing examination malpractices among postgraduate students in selected public tertiary institutions in Rivers State. The study adopted the descriptive survey research design. Three research questions and three hypotheses guided the study. The population of the study consisted of all 9,510 postgraduate students of three academic sessions of three tertiary institutions. Taro Yamane Formula was used for the sample size. The multi-stage sampling techniques were adopted in selecting the sample size of 400. A structured questionnaire titled electronic invigilation inclusion in curbing examination malpractice questionnaire (EIICEMQ) was used as an instrument for data collection. Three experts validated the instrument and Cronbach Alpha Method was used to obtain an average reliability coefficient of 0.83. 400 copies of the questionnaire were administered to the respondents but only 365 were retrieved and used for the study. The items were rated on a four (4) point rating scale; mean and standard deviation were used to answer the research questions while z-test was used in testing the formulated hypotheses. The findings reviewed that there is a high extent of close-circuit television (CCTV) camera, biometric system and signal jamming devices inclusion in curbing examination malpractices among postgraduate students in selected public tertiary institutions in Rivers State. The researcher recommended that tertiary institutions should adopt the use of CCTV camera for surveillance and the recording of examination offenses because the mere presence of CCTV cameras alone is capable of scaring potential malpractice candidates, since they are well aware of being watched, with the possibilities of recorded clips being filed as evidence of malpractice endeavors, tertiary institutions should adopt the use of biometric system in registering admitted students and use same process in identifying the registered students during examinations and tertiary institutions should also deploy signal jamming devices in order to temporarily stop the functionality of cell phones and its usage for unnecessary communications during examinations.

Understanding the risks of China-made CCTV surveillance cameras in Australia

ABSTRACT In the global interconnected economy, China-made information-collecting technologies such as closed-circuit television (CCTV) surveillance cameras have become popular products for routine video-based surveillance. Hikvision and Dahua are the two largest global suppliers of CCTV cameras, with both companies supplying their products to over 200 countries. Despite their popularity, national security concerns are commonly cited when adopting these cameras, citing manufacturer links with the Communist Party of China (CPC), cybersecurity vulnerabilities, and sales recorded in the Xinjiang region, that has records of human rights violations. This paper is structured in three parts: first, we explore the predominance of China-made information-gathering technologies in Australia; second, we summarise common national security concerns usually associated with China-based technology manufacturers; and third, we propose regulatory measures to regulating China-made CCTV cameras in Australia. The paper suggests that while state and Federal decision-makers are free to remove Chinese CCTV surveillance cameras, they should avoid overt politisation. Overall, a stronger focus should be placed on evaluating cybersecurity risks of Internet of Things (IoT) information-collecting technologies and considering their timely and effective regulation from the perspective of individual and national interests.

Consideration of hotspots in the selection of supervision schemes to reduce illegal dumping of construction and demolition waste.

Illegal dumping of construction and demolition waste continues to be a problem hindering circular economy efforts. Controlling illegal dumping necessitates establishing an effective surveillance scheme and investigating the proper penalty value. This problem has previously been described as a game theory problem, with the government and construction contractors as the players. Taking into account illegal dumping hotspots, which can often be identified through topographical and geographical characteristics, is also important when establishing supervision strategies. This study develops an evolutionary game-theoretic model that can assist in devising effective supervision strategies to control the spread of illegal dumping, while taking into consideration hotspots. In specific, this study investigates the suitability of two alternative strategies: police patrols and hybrid monitoring through both patrols and the installation of closed-circuit television cameras in hotspots. The model was applied to two case studies, using parameters informed by real-world contexts, to demonstrate its potential for selecting suitable strategies according to local situations. The results suggest that nine possible scenarios govern the stable evolutionary strategies of game players, with five scenarios in which contractors converge to adopt legal dumping. Accordingly, based on the parameters of the region (hotspot areas, cost of supervision, efficiency of patrols, penalties, etc.), governments would be able to assess which of the strategies would lead to long-term compliance of contractors while also increasing their payoffs. Further analysis allowed to determine the minimum efficiency required, and simulations were performed to demonstrate the influence of different supervision efficiencies and penalties on the evolutionary strategies.

Sediment Transport of Coastal Region Using Time-Series Unmanned Aerial Vehicle Spatial Data

Continuous monitoring of the varying topographical characteristics of shorelines is important for effective coastal management. Closed-circuit television (CCTV) cameras are installed to accumulate photographic data on coastal topographical changes. The overall change in the coastal waters can be intuitively understood from the images. However, the amount of three-dimensional (3D) changes that can be grasped is limited. To address this, studies have employed aerial photogrammetry, which is the use of unmanned aerial vehicles (UAVs) to capture aerial pictures, construct 3D models of target areas, and perform analysis through scale-invariant feature transform and structure from motion technologies. Although highly efficient, this technique requires several ground-control points (GCPs), which could corrupt the overall imagery. This study designs real-time kinematics—global navigation satellite system (RTK–GNSS) UAV, which requires few GCPs. To evaluate the positional accuracy of the captured UAV orthographic images and digital surface models (DSMs) used for precise coastal terrain measurements, a virtual reference service survey was performed to determine the vertical errors. The R-squared was 0.985, which is close to 1.0. Short-term and one-year topographic changes before and after a storm were investigated using time-series UAV image data after a coastal maintenance project. Analysis of the coefficient of variation in the beach volume for one year revealed that submerged breakwater reduced erosion during high wave resistance. The submerged breakwater located in the center exhibited variability similar to the opening. Hence, this method is more suitable for periodically monitoring coastal areas.