IPTV Research Articles

Methodology for Automatically Detecting Pan–Tilt–Zoom CCTV Camera Drift in Advanced Traffic Management System Networks

Many transportation agencies have deployed pan–tilt–zoom (PTZ) closed-circuit television (CCTV) cameras to monitor roadway conditions and coordinate traffic incident management (TIM), particularly in urbanized areas. Pre-programmed “presets” provide the ability to rapidly position a camera on regions of highways. However, camera views occasionally develop systematic deviations from their original presets due to a variety of factors, such as camera change-outs, routine maintenance, drive belt slippage, bracket movements, and even minor vehicle crashes into the camera support structures. Scheduled manual calibration is one way to systematically eliminate these positioning problems, but it is more desirable to develop automated techniques to detect and alert agencies of potential drift. This is particularly useful for agencies with large camera networks, often numbering in the 1000’s. This paper proposes a methodology using the mean Structured Similarity Index Measure (SSIM) to compare images for a current observation to a stored original image with identical PTZ coordinates. Analyzing images using the mean SSIM generates a single value, which is then aggregated every week to generate potential drift alerts. This methodology was applied to 2200 images from 49 cameras over a 12-month period, which generated less than 30 alerts that required manual validation to determine the confirmed drift detection rate. Approximately 57% of those alerts were confirmed to be camera drift. This paper concludes with the limitations of the methodology and future research opportunities to possibly increase alert accuracy in an active deployment.

Genesis of Street Photography in the Context of the Specificity of Digital Age

The purpose of the article is to reveal the peculiarities of the development of the genre of street photography in the context of the digitalisation trend. Research methodology. The logical-analytical, subject-analytical, system-analytical, typological and historical-cultural method, as well as the method of cultural analysis, were applied in order to comprehensively and objectively study the problems of the development of the genre of street photography. Scientific novelty. The genre of street photography is studied through the prism of the features of digital photography; the influence of surrealism, documentary cinema and humanistic tendencies of photo art on the genre of street photography is analysed; the development of the genre of street photography is traced in historical retrospect and at the present stage in the context of the digitalisation trend; little-known foreign language sources of a cultural nature were introduced into scientific circulation. Conclusions. The main characteristics of digital photography are plasticity (the ability to integrate with other media, close connections and interactions between the camera, mobile phones and social networks in the context of the photo sharing process) and the ubiquity of mobile devices (the ability to easily take a photo with a mobile camera), which the specifics of the creative thinking of a modern person and affects the transformation of the genre of street photography. The study found that the main factors influencing the development of street photography in the digital age are: the emergence of smartphones, social media and online sharing, mirrorless cameras and additional functions, artificial intelligence and computational photography. The ubiquity of digital devices and their relationship with various media significantly transforms the genre, which throughout its history has been characterised by a significant reaction to internal (documentary style, surrealism, humanism) and external interventions (for example, consumer culture, popularisation of other genres of photography, lack of control). At the current stage of the development of the genre of street photography, the main influencing factor is the ubiquity of digital photography – access to the camera and urban space create prerequisites for creativity in the genre of street photography, which gradually leads to the levelling of boundaries between professional and amateur photography. The gravitation of the genre of street photography to documentary or art is determined by the author's approach of a particular photographer, the style he follows, the specifics of the environment and technical means. Thanks to the huge amount of photo equipment, the digital age is changing street photography, creating new trends and influencing the organic development of traditional features of the genre, and its adaptive nature allows us to talk about further transformational processes, in accordance with the development of the socio-cultural space.

Analysis of noctilucent clouds’ fields according to ground-based network and airborne photography data

The article analyzes the fields of noctilucent clouds over the territory of the Russian Federation, recorded by a ground-based network of cameras using also aircraft photography, over the two nights in June 2021. It is demonstrated that aircraft photography can significantly improve the coverage of the territory of noctilucent clouds’ probable appearance. The detected noctilucent cloud fields are compared with model regions of water vapor condensation derived from satellite measurements of temperature and water vapor mixing ratio. Practical steps are proposed for the development of aircraft observations of noctilucent clouds.

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.

Digital ergonomic assessment to enhance the physical resilience of human-centric manufacturing systems in Industry 5.0

The emergence of Industry 5.0 promotes the creation of human-centric values. To fulfill this objective, Internet of Things (IoT) technologies are increasingly being exploited to digitize the human factor and monitor the ergonomics of manual manufacturing systems. These digital assessments, combined with computational algorithms, contribute to the establishment of socially inclusive workplaces while offering detailed insights to safeguard the health of the aging workforce. In this scenario, this study proposes a digital architecture for evaluating the European Assembly Worksheet (EAWS) in human-centric manufacturing systems. Three distinct enabling technologies are leveraged to acquire heterogeneous data streams. A radio-frequency-based smart glove detects the operator’s interactions with the surrounding environment, while a network of marker-less cameras and a four-channel surface Electromyography (sEMG) system capture body joint movements and muscular contractions of the upper limbs, respectively. The acquired data are processed by computational algorithms to define an EAWS-driven set of Key Risk Indicators (KRIs), embedded in an ergonomic decision support system. These risk metrics highlight operator-driven process weaknesses in musculoskeletal, muscular, and material handling dimensions. Finally, the validity of the proposed digital architecture is demonstrated in an industrial-related pilot environment, where an operator assembles a piece of home furniture.

Advances and Challenges in Pest Management for Protected Cultivation in India: Integrative Approaches and Future Prospects

Poly house farming in India has transformed agriculture by enabling year-round crop production and protecting crops from adverse weather. However, the controlled environment of poly houses represents unique challenges in pest management, making it crucial to implement effective strategies to maintain crop health and yield. Integrated Pest Management (IPM) offers a comprehensive approach that combines cultural, biological and chemical practices to control pests while minimizing environmental impact. The advantages of protected cultivation include increased productivity, improved produce quality and efficient resource utilization. Despite these benefits, poly house farming faces limitations such as high initial costs, the need for meticulous planning and ongoing crop protection requirements. Pest identification is vital for effective management, as misidentification can lead to inappropriate control measures. Common pests in poly houses include insects, slugs, mites, nematodes, pathogens and weeds. Pest monitoring strategies such as scouting, using monitoring tools and record-keeping are essential for timely and effective management interventions. Management strategies encompass physical, biological and chemical methods. Physical approaches include the use of sticky and pheromone traps, while biological control involves the release of natural predators and entomopathogens. Chemical management involves both biorational pesticides and conventional chemicals, with IPM emphasizing the integration of these methods to reduce reliance on harmful chemicals. Preventive measures like insect-proof screens and soil solarization, along with curative measures and advanced technologies like video camera networks and decision support systems, are pivotal for effective pest control. Future prospects include improving technology standards, enhancing computerized control systems, and fostering research and government support to advance protected cultivation in India.

An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks

Smart cities have developed advanced technology that improves people’s lives. A collaboration of smart cities with autonomous vehicles shows the development towards a more advanced future. Cyber-physical system (CPS) are used blend the cyber and physical world, combined with electronic and mechanical systems, Autonomous vehicles (AVs) provide an ideal model of CPS. The integration of 6G technology with Autonomous Vehicles (AVs) marks a significant advancement in Intelligent Transportation Systems (ITS), offering enhanced self-sufficiency, intelligence, and effectiveness. Autonomous vehicles rely on a complex network of sensors, cameras, and software to operate. A cyber-attack could interfere with these systems, leading to accidents, injuries, or fatalities. Autonomous vehicles are often connected to broader transportation networks and infrastructure. A successful cyber-attack could disrupt not only individual vehicles but also public transportation systems, causing widespread chaos and economic damage. Autonomous vehicles communicate with other vehicles (V2V) and infrastructure (V2I) for safe and efficient operation. If these communication channels are compromised, it could lead to collisions, traffic jams, or other dangerous situations. So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. The proposed Intelligent Intrusion Detection System (IIDS) employs a combination of advanced learning techniques, including Data Fusion, One-Class Support Vector Machine, Random Forest, and k-Nearest Neighbor, to improve detection accuracy. The study demonstrates that the EfficientNet model achieves superior performance with an accuracy of up to 99.97%, highlighting its potential to significantly enhance the security of AV networks. This research contributes to the development of intelligent cyber-security models that align with 6G standards, ultimately supporting the safe and efficient integration of AVs into smart cities.

Establishing a Remote Area Network for an Automation System: A Case Study of Yeni Karpuzlu Drinking Water Automation

Yeni Karpuzlu is a town located in the Ipsala district of Edirne province. The drinking water needs of this town are supplied from a deep water well. Water is pumped from the deep well using a submersible pump and sent to a 500-m 3 capacity concrete water tank. Here, the water goes the town's water demand through natural flow. The water is then sent from the tank, which is located 30 meters above ground level, to the town. There is a distance of 800 meters between the well and the tank, and 6000 meters between the tank and the control center located in the town. A local network has been established using outdoor access points for communication between the industrial control devices located at these three points. Data from the instruments are collected at a speed of 300mbps. Additionally, it is sent to the video center via IP cameras for area security. All devices on this network communicate with each other and are controlled. This paper will discuss the operation of the automation system mentioned above. Operations include the pump operation based on tank levels, pressure control using an adjustable valve to maintain network pressure, and monitoring and control operations from the central unit.

Design and development of a motion activated internet protocol video surveillance system for crowd monitoring

Internet Protocol (IP) cameras are powerful tools in security and crowd monitoring implementation owing to their efficient communication over a wide geographical area. However, for any authorized personnel to initiate recording based on an event, to report a security breach, or even to stop recording when an uninteresting scene is presented, such would have to sit at the reach of the control which is not always possible. In this proposal, we implement a motion activated internet protocol (MAIP) based video surveillance system using network video management for crowd monitoring. The system can activate itself to initiate video recording on detecting motion or event using its in-built features of the network cameras and Video Management Software (VMS). The system will result in an effective and efficient motion detection video surveillance with necessary features that allow for quick recording, conserved bandwidth, greater storage, space conservation, reduces CPU load on recording servers and also allows for integration with other systems.

A hybrid multilinear-linear subspace learning approach for enhanced person re-identification in camera networks

Several tasks in surveillance systems depend on camera networks, one of them is Person re-identification (PRe-ID) which has wide interest as a research topic in the computer vision field. The current techniques in this issue encounter many obstacles in handling the variability of appearance, extracting effective features, and capturing intricate data relationships, leading to suboptimal performance causing systems to have low performance. This paper presents a novel hybrid subspace learning approach for Person re-identification (PRe-ID), that leverages the strengths of both multilinear and linear techniques to significantly enhance the performance of PRe-ID. The proposed process begins by extracting distinctive effective features from pedestrian images using LOMO and GOG as shallow feature descriptors with CNN for deep features, followed by the application of two stages of subspace learning. In the first stage, we employ a multilinear subspace learning algorithm known as Tensor-based Quadratic Discriminant Analysis (TXQDA). TXQDA captures complex relationships and interactions among different features, resulting in a more discriminative feature representation. Subsequently, we employ a linear subspace learning technique called Side Information Linear Discriminant Analysis (SILD) to refine the subspace learned from TXQDA. SILD utilizes linear projections to enhance discriminability and preserve crucial inter-class variations, thereby improving the overall performance of PRe-ID. Ultimately, we implement logistic regression (LR) fusion at the score level to enhance the performance of our hybrid subspace learning approach, particularly within various tensor feature representation scenarios. Extensive experiments are conducted on three challenging datasets to evaluate the effectiveness of our hybrid subspace learning approach. The experimental results demonstrate that our method achieves superior performance compared to state-of-the-art techniques in the field of PRe-ID.

CRRFNet: An adaptive traffic object detection method based on camera and radar radio frequency fusion

A large number of studies have proved that camera and radar fusion is a useful and economical solution for traffic object detection. However, how to improve the reliability and robustness of fusion methods is still a huge challenge. In this paper, an adaptive traffic object detection method based on a camera and radar radio frequency Network (CRRFNet) is proposed, to solve the problem of robust and reliable traffic object detection in noisy or abnormal scenes. Firstly, two different deep convolution modules are designed for extracting features from the camera and radar; Secondly, the camera and radar features are catenated, and a deconvolution module is built for upsampling; Thirdly, the heatmap module is used to compress redundant channels. Finally, the objects in the Field of View (FoV) are predicted by location-based Non-Maximum Suppression (L-NMS). In addition, a data scrambling technique is proposed to alleviate the problem of overfitting to a single sensor by the fusion method. The existing Washington University Camera Radar (CRUW) dataset is improved and a new dataset named Camera-Radar Nanjing University of Science and Technology Version 1.0 (CRNJUST-v1.0) is collected to verify the proposed method. Experiments show that CRRFNet can detect objects by using the information of radar and camera at the same time, which is far more accurate than a single sensor method. Combined with the proposed data scrambling technology, CRRFNet shows excellent robustness that can effectively detect objects in the case of interference or single sensor failure.

Design of an IoT-Based Nursing Assistant Robot Using Nodemcu Esp8266 and Blynk

Work as a nurse requires direct contact with patients, for example when carrying drugs or changing IVs. However, since the COVID-19 pandemic, awareness to reduce direct contact with patients is increasingly needed. Moreover, direct contact with patients with infectious diseases, such as covid-19. For this reason, it is necessary to create a tool / robot that can connect nurses and patients, especially patients with infectious diseases. In this study, a robot design and implementation was made that can assist nurses in delivering drugs to patients, without having to make direct contact with patients. IoT-based designed robots using NodeMCU, ESP8266 and Blynk. NodeMCU ESP8266 is used as a data processor and smartphones are used as remote controls via the internet. Data from the smartphone is processed by NodeMCU then sends a signal to the robot actuator via the L298N driver motor to drive the wheels. Furthermore, the internet-connected IP camera is used for live streaming on smartphones through the V380 Pro application as a directional guide. Then to prevent collisions, the ultrasonic sensor HCSR04 will send data to NodeMCU ESP8266 if there is an obstacle at a distance of 30 cm in front of the robot, so that the robot will move backwards. The experimental results show that the robot can run well where the robot is controlled by a nurse using the Blynk IoT application and V380 Pro on a smartphone via internet media. Then the test was carried out with two methods, namely smartphones using the robot's local Wi-Fi connection and smartphones using cellular networks. The results showed that the response time of the application command on the smartphone to the robot or viewing video results from the IP Camera was affected by the condition of the internet network used.

Eye Tracking Based on Event Camera and Spiking Neural Network

An event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, and addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye-tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of a spiking neural network (SNN) with an event camera, a near-range eye-tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081 ms, enhancing tracking stability while maintaining accuracy.

A Bayesian deep learning approach for video-based estimation and uncertainty quantification of urban rainfall intensity

Accurate, high-resolution spatiotemporal estimates of rainfall intensity (RI) are essential for effective prevention and control of urban flooding. Traditional methods are often costly and provide inadequate coverage. Meanwhile, the associated uncertainty of RI estimates is often overlooked, potentially leading to poor decisions in management of urban floods. Here we examine the potential of video imagery recorded by surveillance cameras in urban areas, for providing real-time estimates of RI. Specifically, we propose the use of Bayesian deep learning (DL) to estimate RI and its related uncertainty in a real-time manner. Our DL approach combines the strengths of convolutional neural network (CNN) and long short-term memory (LSTM) to construct a suitable model for this task, and uses variational inference to quantify the uncertainty of the CNN-LSTM model. The proposed approach is tested using video imagery captured under various light-intensity conditions, including daytime, nighttime, early morning, and early evening, and evaluated via experiments using both random samples and independent rainfall events. Further, we show that the temporal processing provided by the LSTM network is important to achieve good performance of RI estimation. The results indicate the strong potential for leveraging urban camera networks to obtain high-precision spatiotemporal RI estimates at low cost, which can be extremely valuable for implementing effective flood control measures and planning emergency responses in the urban areas.

Near surface camera informed agricultural land monitoring for climate smart agriculture

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

A unified deep semantic expansion framework for domain-generalized person re-identification

Supervised Person Re-identification (Person ReID) methods have achieved excellent performance when training and testing within one camera network. However, they usually suffer from considerable performance degradation when applied to different camera systems. In recent years, many Domain Adaptation Person ReID methods have been proposed, achieving impressive performance without requiring labeled data from the target domain. However, these approaches still need the unlabeled data of the target domain during the training process, making them impractical in many real-world scenarios. Our work focuses on the more practical Domain Generalized Person Re-identification (DG-ReID) problem. Given one or more source domains, it aims to learn a generalized model that can be applied to unseen target domains. One promising research direction in DG-ReID is the use of implicit deep semantic feature expansion, and our previous method, Domain Embedding Expansion (DEX), is one such example that achieves powerful results in DG-ReID. However, in this work we show that DEX and other similar implicit deep semantic feature expansion methods, due to limitations in their proposed loss function, fail to reach their full potential on large evaluation benchmarks as they have a tendency to saturate too early. Leveraging on this analysis, we propose Unified Deep Semantic Expansion, our novel framework that unifies implicit and explicit semantic feature expansion techniques in a single framework to mitigate this early over-fitting and achieve a new state-of-the-art (SOTA) in all DG-ReID benchmarks. Further, we apply our method on more general image retrieval tasks, also surpassing the current SOTA in all of these benchmarks by wide margins.

A New Multi-Branch Convolutional Neural Network and Feature Map Extraction Method for Traffic Congestion Detection.

With the continuous advancement of the economy and technology, the number of cars continues to increase, and the traffic congestion problem on some key roads is becoming increasingly serious. This paper proposes a new vehicle information feature map (VIFM) method and a multi-branch convolutional neural network (MBCNN) model and applies it to the problem of traffic congestion detection based on camera image data. The aim of this study is to build a deep learning model with traffic images as input and congestion detection results as output. It aims to provide a new method for automatic detection of traffic congestion. The deep learning-based method in this article can effectively utilize the existing massive camera network in the transportation system without requiring too much investment in hardware. This study first uses an object detection model to identify vehicles in images. Then, a method for extracting a VIFM is proposed. Finally, a traffic congestion detection model based on MBCNN is constructed. This paper verifies the application effect of this method in the Chinese City Traffic Image Database (CCTRIB). Compared to other convolutional neural networks, other deep learning models, and baseline models, the method proposed in this paper yields superior results. The method in this article obtained an F1 score of 98.61% and an accuracy of 98.62%. Experimental results show that this method effectively solves the problem of traffic congestion detection and provides a powerful tool for traffic management.

Parallel camera network: Motion-compensation vision measurement method and system for structural displacement

This study proposes a motion-compensation vision measurement method and system for structural displacement, termed the parallel camera network (PCN). Based on the fixedly connected calibration cameras and measurement cameras, the PCN method effectively address the optical measurement problems of the unstable observation platform. Unlike existing ego-motion compensation methods, the PCN method allows for reference or stationary points that are distant from the camera stations, significantly reducing motion-induced measurement errors of the observation platform. Laboratory tests showed that compensating for platform motion tripled measurement accuracy using the PCN method. In practical applications, the system has been successfully deployed for monitoring subsidence of operational high-speed rail piers and the arch axis during large-span arch bridge construction. The proposed method and system significantly enhance the engineering flexibility of visual measurements.

Cybersecurity and Forensic Analysis of IP-Cameras Used in Saudi Arabia

In smart city infrastructure, IP cameras play a pivotal role in crime prevention and detection. However, not much research has been conducted on IP cameras from a cybersecurity and forensics perspective. In this study, we investigate vulnerability assessment and forensic artifacts for Hanwha and Mobotix IP cameras, which are widely used in Saudi Arabia. Saudi Arabia is using IP cameras which are essential for its smart cities. In this paper, we examine IP cameras in two directions. The first is to assess the vulnerability of IP cameras through various attack scenarios such as denial of service (DoS), brute force, and unauthorized access, and we suggest countermeasures. The second shows how analysis for IP cameras can be used to investigate logs for cyberattacks. Through this study, we expect to contribute to research on cyber-attack and forensic perspectives on IP cameras to be used in smart cities.

Validation of Camera Networks Used for the Assessment of Speech Movements

Abstract. The term speech sound disorder describes a range of speech difficulties in children that affect speech intelligibility. Differential diagnosis is difficult and reliant on access to validated and reliable measures. Technological advances aim to provide clinical access to measurements that have been identified as beneficial in diagnosing speech disorders. To generate objective measurements and, consequently, automatic scores, the output from multi-camera networks is required to produce quality results. The quality of photogrammetric results is usually expressed in terms of the precision and reliability of the network. Precision is determined at the design stage as a function of the geometry of the network. In this manuscript, we focus on the design of a photogrammetric camera network using three cameras. We adopted a similar workflow as Alsadika et al. (2012) and tested serval network configurations. As the distances from the camera stations to object points were fixed to 3500mm, only the horizontal and vertical placements of the cameras were varied. Horizontal angles were changed within an increment of 10º, and vertical angles were changed within an increment of 5º. The object space coordinates of GCPs for each camera configuration were assessed in terms of horizontal error ellipses and vertical precision. The best design was the maximum horizontal and vertical convergence angles of 90° and 30°. The existing camera network used to capture videos for speech assessment was approximately as good as the top third of tested designs. However, from a validation perspective, it can be concluded that the design is viable for continued use.