IPTV Research Articles

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.

Sub-frame timestamping of a camera network using a coded light signal

To extract the 3D displacements of structures using a camera network, it is essential to know the frame timestamps. However, technical constraints in outdoor conditions can make synchronous frame acquisition impossible. This study aims to reconstruct the timestamps of frames during post-processing to employ cameras without special camera feature. To achieve this, a LED panel that emits a coded light signal is placed in the field of view common to all cameras. The pixel intensity of the LED region in the sequential images is processed to reconstruct each frame’s timestamp. The uncertainty of the timestamp reconstruction is numerically estimated to encode the light signal. Validation was conducted through indoor experiments and outdoor applications to monitor a tower crane. The uncertainty of the obtained timestamp is 1.65% of the frame interval under uncontrolled conditions. The proposed method can be used with cameras, even when synchronization is unattainable.

Student Attendance Management System

The unique traces a real-time face recognition and attendance system designed to streamline attendance tracking in instructive settings. Leveraging OpenCV, face_recognition, and MongoDB, the framework mechanizes the recognizable proof of understudies from video streams or IP camera nourishes, recording their participation precisely and productively. Conventional attendance methods are regularly labor-intensive and inclined to mistakes, inciting the advancement of robotized arrangements. This investigate addresses these challenges by joining strong facial recognition algorithms with real-time video preparing capabilities and adaptable information capacity. By utilizing OpenCV for image processing, face_recognition for facial include location and acknowledgment, and MongoDB for information administration, the framework offers a comprehensive arrangement for participation following. The abstract emphasizes the system's common sense, effectiveness, and potential to revolutionize participation administration in instructive educate. Besides, it highlights the significance of exactness, preparing speed, and versatility in assessing the system's execution. Generally, the theoretical sets the organize for a detailed investigation of the system's design, execution, results, and suggestions for participation administration in educational settings.

Automatic Control of Polymer Dosage Using Floc Images in Sludge Dewatering Plant

This study introduces a novel method for estimating floc conditions in sludge dewatering plants by employing image analysis and automatic control of polymer dosage. While previous research has focused on drinking water treatment plants, few reports address polymer dosage optimization using image analysis in sludge dewatering plants. The challenge lies in the high sludge dry solids hindering individual floc recognition due to overlap. The study aims to estimate floc conditions by focusing on gap areas between flocs and implementing automatic polymer dosage control accordingly. The proposed method uses images from an Internet Protocol camera and semantic segmentation to identify the floc gap area. For validation of the estimation method, variations of over and under polymer dosage scenarios were investigated and compared with commonly used floc area methods. The findings indicate that the gap area estimation effectively reproduces the theory of polymer cohesion. Automatic polymer dosage control based on this method demonstrates stable operation in both scenarios. Notably, automatic control outperformed manual operation during continuous operation, resulting in a significant reduction in polymer dosage and a notable increase in heating efficiency compared to manual control. This study presents an efficient approach to optimize polymer dosage in sludge dewatering plants, utilizing image analysis for real-time monitoring and control. By focusing on the gap area between flocs, the method enhances accuracy in estimating floc conditions, thereby improving overall dewatering efficiency. The findings highlight the practical benefits of implementing automatic control systems in sludge treatment plants, potentially reducing costs and environmental impact.

Development Dilemma and Improvement Measures of IP Film and Television Plays

Development Dilemma and Improvement Measures of IP Film and Television Plays

AN AI-BASED SURVEILLANCE SYSTEM PROPOSAL FOR THE SECOND LINE OF DEFENSE AGAINST IRREGULAR MIGRATION, SMUGGLING, AND TERRORISM: GENDARMERIE ASSESSMENT

Despite the physical and technological measures in place along the border protected by border forces, a significant number of irregular migrants are being apprehended by gendarmerie (Jandarma) elements in the area designated as the second line of defense. This situation poses a crucial responsibility for the Jandarma in the context of preventing irregular migration movements, curbing smuggling activities, and combating terrorism. Therefore, it is proposed that artificial intelligence-supported technological discovery and surveillance measures be implemented in the Jandarma responsibility area behind the border. It is believed that these technological measures could be beneficial in preventing irregular migration movements, restricting smuggling activities, and enhancing effectiveness in the fight against terrorism. The implementation of these measures could contribute to public safety by increasing security and maintaining order. Within the scope of this study, a thermal camera network system powered by solar energy, featuring wireless communication capabilities, and equipped with artificial intelligence analysis, is described. Additionally, the technical architectural features of the system, installation requirements, and details of the artificial intelligence algorithms to be utilized within the system, along with their capabilities and potential algorithm specifics, are explained. The implementation of the proposed system is anticipated to enhance reconnaissance and surveillance capabilities.

Teknologi Kendali Kamera Fpv 2 DOF Melalui Gesture Kepala Dengan Sensor IMU (Inertial Measurement Unit)

In taking videos or photos while in the air, there are currently very many tools or media that can be used and with the current technology that is very rapidly developing, especially in science, it can do everything easily. In the electronic field, a drone is a device that can fly and be controlled unmanned using a remote control. This object has a designation that is called a UAV (Unmanned Aerial Vehicle). This drone has a variety starting from the shape, size, different characters, and functions. FPV is one of the important parts of the 2DOF FPV (first person view) drone as a head gesture combined with parts such as nano arduino, Blueotooth HC-04, Servo, servo tilt up, IP camera, nano arduino must be connected to bluetooth on the cellphone, servo functions as moving right and left in accordance with the user's head gesture, servo tilt up functions as a gesture from top to bottom according to the gesture of the user and IP camera functions as video streaming, VR Glasses or commonly called Googles are connected using HC-04 bluetooth. According to the function in VR Glasses, it will bring up the sensitivity and movement value on the servo and servo tilt up and display the camera display on the IP camera.

Intelligent Infrastructure for Traffic Monitoring Based on Deep Learning and Edge Computing

In the field of traffic management and control systems, we are witnessing a symbiotic evolution, where intelligent infrastructure is progressively collaborating with smart vehicles to produce benefits for traffic monitoring and security, by rapidly identifying hazardous behaviours. This exponential growth is due to the rapid development of deep learning in recent years, as well as the improvements in computer vision models. These technologies allow for monitoring tasks without the need to install numerous sensors or stop the traffic, using the extensive camera network of surveillance cameras already present in worldwide roads. This study proposes a computer vision-based solution that allows for real-time processing of video streams through edge computing devices, eliminating the need for Internet connectivity or dedicated sensors. The proposed system employs deep learning algorithms and vision techniques that perform vehicle detection, classification, tracking, speed estimation, and vehicle geolocation.

Advanced Traffic Clearance System for Emergency Vehicles

This paper elaborates development of Traffic Clearance System using camera and mobile Net. In urban areas, navigating through traffic congestion poses a significant challenge for emergency vehicles, often leading to delays that can impact response times and potentially endanger lives. This paper presents an innovative traffic control system leveraging camera technology to prioritize the passage of emergency vehicles. The proposed system utilizes a network of cameras strategically placed at intersections to detect emergency vehicle sirens and flashing lights. Upon detection, the system dynamically adjusts traffic signal timing to create a green corridor, facilitating the swift and safe passage of emergency vehicles. Additionally, the system incorporates machine learning algorithms to optimize traffic flow, minimize congestion, and ensure efficient utilization of road space. Through simulation studies and real-world testing, the effectiveness and reliability of the proposed system are evaluated, demonstrating its potential to enhance emergency response capabilities and improve overall traffic management in urban environments.

Remotely controlled smart monitoring system of hermetic paddy storage to reduce postharvest losses in Bangladesh

In Bangladesh, where agriculture forms the backbone of the economy, efficient grain storage is crucial, particularly for paddy storage. Traditional methods like gunny bags and plastic containers are common, but the recent introduction of hermetic storage technologies, such as GrainPro bags and hermetic cocoons, have significantly reduced postharvest losses paddy. However, most research focuses on oxygen (O2) and carbon dioxide (CO2) levels in those open storage systems. This study introduces an innovative, cost-effective remote monitoring system for hermetically sealed grain storage, designed using locally sourced components, including a Digital Hygrometer, WiFi Mini IP Camera, and a Remote Controller unit. Tested over a 125-day period at Advanced Storage Lab, Bangladesh Agricultural University, the system effectively monitored the internal environment of the storage units. Key findings show that the internal temperature of the storage bags varied between 16.7°C and 29.2°C, demonstrating lesser fluctuation compared to the ambient temperature range of 16.1°C to 33.5°C. The relative humidity inside the storage units remained stable between 45 % and 54 %, in contrast to the external humidity range of 45 % to 81.2 %. Additionally, using the Steffe and Singh equation, an increase of 0.512 % in Equilibrium Moisture Content (EMC) of paddy was recorded. Notably, there was no evidence of insect or mold growth after 125 days of storage. This study's remote monitoring system not only marks a significant advancement in hermetic grain storage technology but also contributes to sustainable agricultural practices. It provides a practical, real-time solution to monitor and manage key environmental parameters, ensuring the preservation of grain quality over time.

Energy Production Optimization by Positioning of Solar Panels and Surveillance System with Cleaning of Panels

The pursuit of efficient and sustainable energy generation has led to the widespread adoption of solar panels. However, the optimal performance of these panels can be hindered by factors such as dirt accumulation, suboptimal orientation, and security concerns. To address these challenges, we propose an integrated Solar Panel Tracking, Cleaning, and Surveillance System (SPTCSS). Our system employs advanced tracking technology to optimize solar panel orientation, ensuring maximum exposure to sunlight throughout the day. Additionally, it incorporates automated cleaning mechanisms, utilizing water and brushes to remove dust, debris, and other contaminants, thereby maintaining peak efficiency. Furthermore, the surveillance aspect of the system provides real-time monitoring and security features, safeguarding against theft, vandalism, and unauthorized access. Through a network of sensors and cameras, any anomalies or suspicious activities can be promptly detected and addressed. By integrating tracking, cleaning, and surveillance functionalities into a single system, our solution offers a comprehensive approach to enhancing the performance, longevity, and security of solar panel installations. This not only maximizes energy generation but also minimizes maintenance requirements and mitigates risks, ultimately contributing to a more sustainable and reliable renewable energy infrastructure

A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams.

This study outlines a method for using surveillance cameras and an algorithm that calls a deep learning model to generate video segments featuring salmon and trout in small streams. This automated process greatly reduces the need for human intervention in video surveillance. Furthermore, a comprehensive guide is provided on setting up and configuring surveillance equipment, along with instructions on training a deep learning model tailored to specific requirements. Access to video data and knowledge about deep learning models makes monitoring of trout and salmon dynamic and hands-on, as the collected data can be used to train and further improve deep learning models. Hopefully, this setup will encourage fisheries managers to conduct more monitoring as the equipment is relatively cheap compared with customized solutions for fish monitoring. To make effective use of the data, natural markings of the camera-captured fish can be used for individual identification. While the automated process greatly reduces the need for human intervention in video surveillance and speeds up the initial sorting and detection of fish, the manual identification of individual fish based on natural markings still requires human effort and involvement. Individual encounter data hold many potential applications, such as capture-recapture and relative abundance models, and for evaluating fish passages in streams with hydropower by spatial recaptures, that is, the same individual identified at different locations. There is much to gain by using this technique as camera captures are the better option for the fish's welfare and are less time-consuming compared with physical captures and tagging.

A computer vision system for apple fruit sizing by means of low-cost depth camera and neural network application

AbstractFruit size is crucial for growers as it influences consumer willingness to buy and the price of the fruit. Fruit size and growth along the seasons are two parameters that can lead to more precise orchard management favoring production sustainability. In this study, a Python-based computer vision system (CVS) for sizing apples directly on the tree was developed to ease fruit sizing tasks. The system is made of a consumer-grade depth camera and was tested at two distances among 17 timings throughout the season, in a Fuji apple orchard. The CVS exploited a specifically trained YOLOv5 detection algorithm, a circle detection algorithm, and a trigonometric approach based on depth information to size the fruits. Comparisons with standard-trained YOLOv5 models and with spherical objects were carried out. The algorithm showed good fruit detection and circle detection performance, with a sizing rate of 92%. Good correlations (r > 0.8) between estimated and actual fruit size were found. The sizing performance showed an overall mean error (mE) and RMSE of + 5.7 mm (9%) and 10 mm (15%). The best results of mE were always found at 1.0 m, compared to 1.5 m. Key factors for the presented methodology were: the fruit detectors customization; the HoughCircle parameters adaptability to object size, camera distance, and color; and the issue of field natural illumination. The study also highlighted the uncertainty of human operators in the reference data collection (5–6%) and the effect of random subsampling on the statistical analysis of fruit size estimation. Despite the high error values, the CVS shows potential for fruit sizing at the orchard scale. Future research will focus on improving and testing the CVS on a large scale, as well as investigating other image analysis methods and the ability to estimate fruit growth.

Real-Time Video Analysis for Intelligent Traffic Management, Public Safety, and Security: A Deep Learning Approach

This paper proposes a novel centralized traffic management system designed to enhance road safety and streamline law enforcement. The system leverages a network of intelligent sensors and cameras to achieve real-time accident detection, traffic violation identification, and facial recognition. Data collected from these sensors is fed into a centralized hub for processing and analysis. Upon identifying an incident, the system promptly dispatches notifications to patrolling units, enabling faster response times for accidents and violations. Additionally, facial recognition capabilities allow for real-time identification of individuals, potentially aiding in apprehending criminals or locating missing persons. This paper will discuss the system architecture, core functionalities, and potential benefits for improved traffic management and public safety. We will also address the challenges associated with data privacy and security in such a centralized system.

3D pose estimation using joint-based calibration in distributed RGB-D camera system

This paper proposes a new approach that acquires camera parameters and generates an integrated 3D joint using an RGB-D camera network distributed in an arbitrary location in space. The proposed technique consists of three steps. In the first step, camera parameters are calculated using partial joints as feature points. The internal and external parameters between cameras are not calculated using a specially manufactured calibration plate. In the second step, a 3D joint set is estimated by integrating the joints obtained from each camera using the calculated camera parameters. At the same time, a 3D volumetric model in the form of a point cloud is reconstructed. The third step consists of a joint correction algorithm. The resultant 3D joint with high reliability is estimated by correcting the position of the joint using the 3D point cloud reconstructed previously. The generated 3D joint can accurately express the shape and movement of 3D human. The estimated 3D joint was compared with the joint measured using a motion capture device to evaluate its performance. The temporal standard deviation between two measurements shows very low value from 1.966 mm to 7.99 mm.

Exploring Convolutional Neural Networks for the Thermal Image Classification of Volcanic Activity

This paper addresses the classification of images depicting the eruptive activity of Mount Etna, captured by a network of ground-based thermal cameras. The proposed approach utilizes Convolutional Neural Networks (CNNs), focusing on pretrained models. Eight popular pretrained neural networks underwent systematic evaluation, revealing their effectiveness in addressing the classification problem. The experimental results demonstrated that, following a retraining phase with a limited dataset, specific networks such as VGG-16 and AlexNet, achieved an impressive total accuracy of approximately 90%. Notably, VGG-16 and AlexNet emerged as practical choices, exhibiting individual class accuracies exceeding 90%. The case study emphasized the pivotal role of transfer learning, as attempts to solve the classification problem without pretrained networks resulted in unsatisfactory outcomes.

Aligning security and energy-efficiency using change detection and partial encryption for wireless camera networks

Wireless camera networks (WCNs) have seen widespread deployment in recent years, leading to an upsurge in monitoring and surveillance systems aimed at detecting foreign objects and changes within remote environments. However, wireless communication remains susceptible to cybersecurity threats, enabling eavesdroppers to access and manipulate confidential multimedia data effortlessly. Moreover, wireless cameras are low-power devices, encounter challenges as many state-of-the-art schemes adopt intricate security mechanisms, resulting in elevated costs, latency, and power consumption. This significantly compromises the stability of the node power and network lifetime and limits the applicability of security schemes in real-time implementations. This study addresses these issues by presenting DyimEnc, a dynamic image encryption scheme, to establish secure image transmission within wireless camera networks. The proposed scheme dynamically implements a security, complexity, and power consumption trade-off based on the currently available resources in each camera node. In the DyimEnc scheme, an effective in-node change-detection method is used to detect changes in the captured images and reduce the false transmission of images across the network. Additionally, a dynamic lightweight partial-encryption method is proposed to encrypt the edge maps of sensitive images that include changes. Moreover, a flexible key-management method is proposed for image encryption and transmission. Simulation results underscore DyimEnc's substantial impact, revealing significant reductions in power consumption, encryption cost, and transmission latency. It effectively achieves an average throughput of 254.9 KB/s and a remarkable 48 % decrease in network power consumption, showcasing a substantial 59 % improvement compared to a related study. Security analysis proved the robustness of our scheme in resisting attacks and satisfying the security requirements of WCNs.

Multi-camera Vehicle Tracking and Recognition with Multimodal Contrastive Domain Sharing GAN and Topological Embeddings

Tracking vehicles across a city using a network of multiple cameras are pivotal for enhancing urban and traffic management systems. However, this task is riddled with challenges such as wide geographical coverage, frequent view obstructions, and the diverse appearances of vehicles from various angles.
 To address these complexities, the proposed solution, dubbed Overlapped Vehicle Detection and Tracking using Multimodal Contrastive Domain Sharing Generative Adversarial Network optimized with Efficient Multi-camera system (MCDS-GAN), leverages cutting-edge techniques from computer vision, image processing, machine learning, and sensor fusion. This advanced system detects and tracks vehicles even in scenarios where multiple camera views overlap, making it applicable across domains like traffic management, surveillance, and autonomous vehicles.
 The methodology involves utilizing datasets like Common Objects in Context and ImageNet for training. Detection and tracking are performed using the Multimodal Contrastive Domain Sharing Generative Adversarial Network, followed by vehicle re-identification facilitated by the Topological Information Embedded Convolution Neural Network (TIE-CNN).
 Moreover, optimization techniques are employed to ensure synchronization and efficiency within the system. Implemented in Python, the effectiveness of MCDS-GAN is rigorously evaluated using metrics such as Accuracy, Precision, Recall, Latency, Response Time, and Scalability. Simulation results showcase its superiority, achieving significantly higher accuracy rates compared to existing methods such as OC-MCT-OFOV, MT-MCT-VM-CLM, and TI-VRI.

Unidirectional switch model for multiservice traffic with polymodal distribution

The modern stage of development of telecommunication networks is characterized by active improvement of routing algorithms in packet-switched networks taking into account the provision of the required quality of service. The need to transfer large amounts of information is due to the need to ensure the normal functioning of modern multimedia applications, in particular such as IP telephony, video and radio broadcasting, interactive distance learning, which determines the multiservice nature of traffic, which is heterogeneous, and its statistical properties in most cases are of a complex polymodal nature. Taking into account the fact that traditional Poisson models providing FIFO servicing of network traffic for solving modern telecommunication problems lead to overly optimistic results in terms of delays, there is a need to develop a new mathematical model of a unidirectional switch providing FIFO servicing of multiservice traffic with polymodal distribution taking into account the provision of the required quality of subscriber service and an algorithm for estimating its main parameters. Development of a mathematical model of a unidirectional switch FIFO-service of multiservice traffic with polymodal distribution taking into account the provision of the required quality of subscriber service and an algorithm for estimating its main parameters. A new mathematical model of a unidirectional switch implementing FIFO-service of multiservice traffic with polymodal distribution has been developed, allowing one to estimate the probabilities that packets arriving at the switch input will not be processed due to overflow of its buffer memory and exceeding the maximum packet processing time. The model takes into account the delays introduced by the channel and channel equipment during the distribution and processing of packets from the source to the switch in question. Recommendations for modeling traffic processing to ensure the specified accuracy of the results are presented.