CCTV Research Articles

Deep Neural Network based Anomaly Detection for Real Time Video Surveillance

One of the main concerns across all kinds of domains has always been security. With the crime rates increasing every year the need to control has become crucial. Among the various methods present to monitor crime or any anomalous behavior is through video surveillance. Nowadays security cameras capture incidents in almost all public and private place if desired. Even though we have abundance of data in the form of videos they need to be analyzed manually. This results in long hours of manual labour and even small human discrepancies may have huge consequences negatively. For this purpose, a Convolution Neural Network (CNN) based model is built to detect any form of abnormal activities or anomalies in the video footages. This model converts the input video into frames and detects the anomalous frames. To increase the efficiency of the model, the data is de-noised with Gaussian blur feature. The avenue dataset is used in this work to detect and predict various kinds of anomalies. The performance of the model is measured using classification accuracy and the results are reported.

INFORMATION TECHNOLOGY OF FACIAL EMOTION RECOGNITION FOR VISUAL SAFETY SURVEILLANCE

Emotional expressions serve a crucial role in interpersonal communication between people while improving social life. In particular, information safety systems for visual surveillance that aim to recognize human emotional facial states are highly relevant today. In this regard, this study is devoted to the problem of identifying the main criteria for expressing the face of emotional manifestations for the possibility of recognizing without the use of specialized equipment, for example, security surveillance cameras with low resolution. In this work, we propose informational technology to define the face’s areas that reproduce the face’s emotional look. The input data from the proposed information technology is a set of videos with detected faces with the primary emotional states reproduced on them. At first, normalization of the faces of images is conducted to compare them in one base. It is executed by centering the face area and normalizing the distance between the eyes. Based on the analysis of point features moving in the set of input images, information points are then allocated (i.e., those points whose movement in the person’s emotional expression is the most significant). At the final stage, the areas of the face (with different bias thresholds) are determined, the changes of which form a visual perception of emotions. For each selected region, a set of possible states is formed. In conclusion, the behavior of point-specific features of a person under the manifestation of specific emotions is explored experimentally, and high-quality indicators for these emotions are highlighted. According to the study results, it is preferable to create a software product based on qualitative criteria for assessing the main areas of the face to determine the mimic expression of emotions.

A Study on Abnormal Behavior Detection in CCTV Images through the Supervised Learning Model of Deep Learning

A Study on Abnormal Behavior Detection in CCTV Images through the Supervised Learning Model of Deep Learning

Application of Modern Technology of LIDAR in Checking Verticality of Piles

LIDAR imaging systems are one of the hottest topics in the optronics industry. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. LIDAR has various applications in today’s industrial world as security cameras, event lighting, in autonomous vehicles, etc. which enables a picturesque images of projected area along with distance. It’s widely used in thick forest surveys which produces better results as compare to photogrammetry. It has been used for surveying a large area but not into the construction repairs and maintenance industry. This paper represents the application of LIDAR system in the distress management and repairs and rehabilitation of an off shore structure which got disrupted in tauktae in 2021 and by using the technology an attempt to check the verticality of piles of jetty is made. LIDAR data have been proven beneficial in the last few years, since it provides information like the height and properties of objects, statistics for wide areas, and it all becomes available by recording the intensity of backscattered pulse in addition to the 3D coordinates. Using the different types of LIDAR help us to discover places we could not see before, like top of mountains, sea floors and other places in our wide world, depending on airborne system or bathymetric system. In this paper, an overview of LIDAR components and basic principle to measure eccentricity in piles are provided and the methodology is explained. Also, a list of the effective parameters which cause errors while collecting data is provided, and how to minimize the error of estimated values by processing many correction steps. Finally, for extraction of eccentricity occurred in piles of jetty structure in the region of Maharashtra and Gujrat in Arabian sea, the obtained data is overlapped with original data and eccentricity is calculated.

Cybersecurity and Web of Things: A review on Ad hoc issues

Abstract: Cyber Security is employed for shielding knowledge systems like networks, computers, databases, information centers, and applications with acceptable procedural and technical security measures. As most of the issues pop up the thoughts, ‘cybercrimes’ can mix colossally daily. Crime is rising as a grave threat in today’s world. It’s an aggressive space of crime. As the number of web users has grown, so has offense. For example, the current Covid-19 pandemic has had a notable consequence for organizations and establishments, resulting in security vulnerabilities; as a result, network security is becoming progressively diverse and complex, and as a matter of fact, completely different ways are being devised to exploit it. Network engineers got to sustain with recent advances in each hardware and software package field to forestall them from being employed, together with user information. Historically, solely mobile computers and phones were connected to the internet; however, with the advancement of technology, other items such as security cameras, microwaves, automobiles, and industrial machinery are all now connected to the internet. The web of stuff is the term given to this network of interconnected things. Prime Securities for the Web of Things are involved[1]. This paper gives detailed information on cyber security and crime as little more than a result. It addresses forms of cyber security, the need for cyber security, issues regarding cyber security, benefits and drawbacks, criminal background, and criminal offenses. Keywords: Computer Security, Internet, Protection, Application software, IP network, Exploits, Malware Threats, Virus, IoT, Network Security, Computer Network, Cybersecurity.

Performing DoS Attacks on Bluetooth Devices Paired with Google Home Mini

In today’s technology world, Virtual personal assistants (VPA) have become very common and most people have started making their homes smart using these VPAs. Although different companies have different assistants, Google Home Mini (GHM) will be our focus in this paper. The first device, Google Home, was released in November 2016 and then GHM was released after a year, in October 2017. GHM has many features such as playing music, setting reminders, setting kitchen timer and controlling smart home devices. Although GHM might be reliable against cyber-attacks, devices, which are paired with GHM, could be attacked and these cyber-attacks can lead to severe problems. Cyber-attack issues become more important to us, specifically if the devices controlled by GHM are vital devices such as oven, fire alarm, security camera. In this article, we will represent Denial of Service (DOS) attacks applied against devices which are paired with GHM. In this study, Bluedoser, L2ping, Bluetooth Dos Script which are software in Kali Linux platform were used in order to perform DoS attack and some devices were used such as Google Home Mini, Headphone, 2 speakers as victim device. Successful results were observed on Bluetooth headphone.

PELATIHAN KEAMANAN DAN KESELAMATAN DI DESTINASI WISATA SESUAI STANDAR NASIONAL MAUPUN INTERNASIONAL BAGI PENGELOLA PARIWISATA DI KABUPATEN SUKOHARJO

Absrtact : Organizing Security and Safety training in tourist destinations, participants can understand security and safety both national and international standards, plan the right form of security and safety management in tourism destinations in general by adjusting the situation and conditions in tourism destinations, evaluate the extent to which security and safety practices are implemented. safety in tourism destinations, encouraging the sustainability of the application in tourist destinations. The training method provided is theory and practice adapted to the hotel area. The results obtained from participants can master the security and safety system of tourist areas, CCTV systems, fire extinguishing systems, and guest room security systems for home stay and hotel managers.

Don’t Deputize Intolerance: Keeping Your Security Policies Safe from Your Patrons

To live in rural Oregon is to live in tension. Crook County exemplifies the tensions of living in rural Oregon in many ways, and not just because it is located dead center in the middle of the state. It also encapsulates the contradiction of some residents trying to keep a hold on a past they perceive as idyllic, while others live with the opportunities and harsh realities of the present. Crook County sees this contradiction reflected in its reliance on industries both historic and modern: ranching, wood products, and auto tires on the one hand, and data centers, health care, and hemp on the other. This tension can boil over into conflict, even when it comes to something as supposedly simple as a change in library policy. Like in many other communities suffering identity crises, some people in Crook County, and its only incorporated town of Prineville, ran afoul of the rising use of opioids (Chaney, 2019). Those of us at the public library saw the effects firsthand. In 2018 and 2019, the library faced a confluence of opioid-adjacent situations. These incidents presented a serious security dilemma for the library where we worked as director and assistant director: How do we ensure safety for the most vulnerable patrons, including those experiencing adverse effects from drugs, while generally keeping the library welcoming for everyone? This dilemma led us to two security-related decisions: to forbid sleeping in the library and to install security cameras. Both decisions ultimately demonstrated how choices made, ostensibly, to protect patrons' physical safety, or to help some people feel more "secure," can adversely impact safety for patrons who are already marginalized.

Application of Modern Technology of LIDAR in Checking Verticality of Piles

LIDAR imaging systems are one of the hottest topics in the optronics industry. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. LIDAR has various applications in today’s industrial world as security cameras, event lighting, in autonomous vehicles, etc. which enables a picturesque images of projected area along with distance. It’s widely used in thick forest surveys which produces better results as compare to photogrammetry. It has been used for surveying a large area but not into the construction repairs and maintenance industry. This paper represents the application of LIDAR system in the distress management and repairs and rehabilitation of an off shore structure which got disrupted in tauktae in 2021 and by using the technology an attempt to check the verticality of piles of jetty is made. LIDAR data have been proven beneficial in the last few years, since it provides information like the height and properties of objects, statistics for wide areas, and it all becomes available by recording the intensity of backscattered pulse in addition to the 3D coordinates. Using the different types of LIDAR help us to discover places we could not see before, like top of mountains, sea floors and other places in our wide world, depending on airborne system or bathymetric system. In this paper, an overview of LIDAR components and basic principle to measure eccentricity in piles are provided and the methodology is explained. Also, a list of the effective parameters which cause errors while collecting data is provided, and how to minimize the error of estimated values by processing many correction steps. Finally, for extraction of eccentricity occurred in piles of jetty structure in the region of Maharashtra and Gujrat in Arabian sea, the obtained data is overlapped with original data and eccentricity is calculated.

Efficient Violence Detection in Surveillance.

Intelligent video surveillance systems are rapidly being introduced to public places. The adoption of computer vision and machine learning techniques enables various applications for collected video features; one of the major is safety monitoring. The efficacy of violent event detection is measured by the efficiency and accuracy of violent event detection. In this paper, we present a novel architecture for violence detection from video surveillance cameras. Our proposed model is a spatial feature extracting a U-Net-like network that uses MobileNet V2 as an encoder followed by LSTM for temporal feature extraction and classification. The proposed model is computationally light and still achieves good results—experiments showed that an average accuracy is 0.82 ± 2% and average precision is 0.81 ± 3% using a complex real-world security camera footage dataset based on RWF-2000.

A Vision-Based System for In-Sleep Upper-Body and Head Pose Classification.

Sleep quality is known to have a considerable impact on human health. Recent research shows that head and body pose play a vital role in affecting sleep quality. This paper presents a deep multi-task learning network to perform head and upper-body detection and pose classification during sleep. The proposed system has two major advantages: first, it detects and predicts upper-body pose and head pose simultaneously during sleep, and second, it is a contact-free home security camera-based monitoring system that can work on remote subjects, as it uses images captured by a home security camera. In addition, a synopsis of sleep postures is provided for analysis and diagnosis of sleep patterns. Experimental results show that our multi-task model achieves an average of 92.5% accuracy on challenging datasets, yields the best performance compared to the other methods, and obtains 91.7% accuracy on the real-life overnight sleep data. The proposed system can be applied reliably to extensive public sleep data with various covering conditions and is robust to real-life overnight sleep data.

Analysis of Image Thresholding Algorithms for Automated Machine Learning Training Data Generation

Secured compounds often safeguard physical layout details of both internal and external facilities, but these details are at risk due to the growing inclusion of Light Detection and Ranging (LiDAR) sensors in consumer off-the-shelf (COTS) technology such as cell phones. The ability to record detailed distance data with cell phones facilitates the production of high-quality three-dimensional scans in a discrete manner which directly threatens the security of private compounds. Therefore, it behooves the organizations in charge of private compounds to detect LiDAR activity. Many security cameras already detect LiDAR sources as generic light sources in specific conditions, but further analysis must identify these light sources as LiDAR sources in order to alert an organization of a potential security incident. Testing confirms the feasibility of identifying some LiDAR sources based on the color and intensity of light shined directly into a camera sensor, but this analysis proves inadequate for cell phone LiDAR. However, the unique intensity and pattern characteristics of cell phone LiDAR reflected off a surface can potentially be identified by an object identification machine learning model. In order to train a model to identify a LiDAR object, we must first produce a training dataset containing marked and labelled LiDAR objects. To do this, we apply an image thresholding algorithm to isolate the LiDAR object in an image to calculate its bounding box. The image thresholding algorithm directly affects the bounding box accuracy, so we test two different algorithms and find that Otsu’s image thresholding algorithm performs best, resulting in 99.5% accurate bounding boxes.

Radical Welcome in the Archives: What Does It Really Mean to Say “You're Welcome Here”?

Radical Welcome in the Archives: What Does It Really Mean to Say “You're Welcome Here”?

Airborne Transmission of SARS-CoV-2 Delta Variant within Tightly Monitored Isolation Facility, New Zealand (Aotearoa).

In New Zealand, international arrivals are quarantined and undergo severe acute respiratory syndrome coronavirus 2 screening; those who test positive are transferred to a managed isolation facility (MIF). Solo traveler A and person E from a 5-person travel group (BCDEF) tested positive. After transfer to the MIF, person A and group BCDEF occupied rooms >2 meters apart across a corridor. Persons B, C, and D subsequently tested positive; viral sequences matched A and were distinct from E. The MIF was the only shared location of persons A and B, C, and D, and they had no direct contact. Security camera footage revealed 4 brief episodes of simultaneous door opening during person A’s infectious period. This public health investigation demonstrates transmission from A to B, C, and D while in the MIF, with airborne transmission the most plausible explanation. These findings are of global importance for coronavirus disease public health interventions and infection control practices.

Composition of an automated attendance register of students by security cameras, as part of a smart city

Composition of an automated attendance register of students by security cameras, as part of a smart city

Hybrid deep-learning model to detect botnet attacks over internet of things environments

In recent years, the use of the internet of things (IoT) has increased dramatically, and cybersecurity concerns have grown in tandem. Cybersecurity has become a major challenge for institutions and companies of all sizes, with the spread of threats growing in number and developing at a rapid pace. Artificial intelligence (AI) in cybersecurity can to a large extent help face the challenge, since it provides a powerful framework and coordinates that allow organisations to stay one step ahead of sophisticated cyber threats. AI provides real-time feedback, helping rollover daily alerts to be investigated and analysed, effective decisions to be made and enabling quick responses. AI-based capabilities make attack detection, security and mitigation more accurate for intelligence gathering and analysis, and they enable proactive protective countermeasures to be taken to overwhelm attacks. In this study, we propose a robust system specifically to help detect botnet attacks of IoT devices. This was done by innovatively combining the model of a convolutional neural network with a long short-term memory (CNN-LSTM) algorithm mechanism to detect two common and serious IoT attacks (BASHLITE and Mirai) on four types of security camera. The data sets, which contained normal malicious network packets, were collected from real-time lab-connected camera devices in IoT environments. The results of the experiment showed that the proposed system achieved optimal performance, according to evaluation metrics. The proposed system gave the following weighted average results for detecting the botnet on the Provision PT-737E camera: camera precision: 88%, recall: 87% and F1 score: 83%. The results of system for classifying botnet attacks and normal packets on the Provision PT-838 camera were 89% for recall, 85% for F1 score and 94%, precision. The intelligent security system using the advanced deep learning model was successful for detecting botnet attacks that infected camera devices connected to IoT applications.

How effective are residential CCTV systems: Evaluating the impact of natural versus mechanical surveillance on house break-ins and theft in hotspots of Penang Island, Malaysia

How effective are residential CCTV systems: Evaluating the impact of natural versus mechanical surveillance on house break-ins and theft in hotspots of Penang Island, Malaysia

Nuclear radiation detection based on the convolutional neural network under public surveillance scenarios

Abstract Nuclear energy is a clean and popular form of energy, but leakage and loss of nuclear material pose a threat to public safety. Radiation detection in public spaces is a key part of nuclear security. Common security cameras equipped with complementary metal oxide semiconductor (CMOS) sensors can help with radiation detection. Previous work with these cameras, however, required slow, complex frame-by-frame processing. Building on the previous work, we propose a nuclear radiation detection method using convolution neural networks (CNNs). This method detects nuclear radiation in changing images with much less computational complexity. Using actual video images captured in the presence of a common Tc-99m radioactive source, we construct training and testing sets. After training the CNN and processing our test set, the experimental results show the high performance and effectiveness of our method.

Unlocking the Potential of Deep Learning for Migratory Waterbirds Monitoring Using Surveillance Video

Estimates of migratory waterbirds population provide the essential scientific basis to guide the conservation of coastal wetlands, which are heavily modified and threatened by economic development. New equipment and technology have been increasingly introduced in protected areas to expand the monitoring efforts, among which video surveillance and other unmanned devices are widely used in coastal wetlands. However, the massive amount of video records brings the dual challenge of storage and analysis. Manual analysis methods are time-consuming and error-prone, representing a significant bottleneck to rapid data processing and dissemination and application of results. Recently, video processing with deep learning has emerged as a solution, but its ability to accurately identify and count waterbirds across habitat types (e.g., mudflat, saltmarsh, and open water) is untested in coastal environments. In this study, we developed a two-step automatic waterbird monitoring framework. The first step involves automatic video segmentation, selection, processing, and mosaicking video footages into panorama images covering the entire monitoring area, which are subjected to the second step of counting and density estimation using a depth density estimation network (DDE). We tested the effectiveness and performance of the framework in Tiaozini, Jiangsu Province, China, which is a restored wetland, providing key high-tide roosting ground for migratory waterbirds in the East Asian–Australasian flyway. The results showed that our approach achieved an accuracy of 85.59%, outperforming many other popular deep learning algorithms. Furthermore, the standard error of our model was very small (se = 0.0004), suggesting the high stability of the method. The framework is computing effective—it takes about one minute to process a theme covering the entire site using a high-performance desktop computer. These results demonstrate that our framework can extract ecologically meaningful data and information from video surveillance footages accurately to assist biodiversity monitoring, fulfilling the gap in the efficient use of existing monitoring equipment deployed in protected areas.

Programming big data analysis: principles and solutions

In the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. This data, commonly referred to as Big Data, is challenging current storage, processing, and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from Big Data. Most of the recent surveys provide a global analysis of the tools that are used in the main phases of Big Data management (generation, acquisition, storage, querying and visualization of data). Differently, this work analyzes and reviews parallel and distributed paradigms, languages and systems used today to analyze and learn from Big Data on scalable computers. In particular, we provide an in-depth analysis of the properties of the main parallel programming paradigms (MapReduce, workflow, BSP, message passing, and SQL-like) and, through programming examples, we describe the most used systems for Big Data analysis (e.g., Hadoop, Spark, and Storm). Furthermore, we discuss and compare the different systems by highlighting the main features of each of them, their diffusion (community of developers and users) and the main advantages and disadvantages of using them to implement Big Data analysis applications. The final goal of this work is to help designers and developers in identifying and selecting the best/appropriate programming solution based on their skills, hardware availability, application domains and purposes, and also considering the support provided by the developer community.