IPTV Research Articles

Analysis of User Behavior using Set-Top-Box for Internet Protocol Television-Based Application

The growth of Internet usage has grown day by day. Many people use the internet for their commercial, education, and personal needs. Misuse of the internet has also increased exponentially. As with the rapid growth of Internet Protocol Television (IPTV), a high alarm still prevails. Much fake news and information are propagated through social media, which misguides many people. Hence tracing back of IP address is needed for appropriate monitoring. The log history stored in the service provider does not enrich the parents to monitor the same. Cache memory involved in the service provider is shared with needy people at their request. Retaining information in service providers depends upon the local norms of any region. A large amount of data handling in the service provider increases the challenges. This paper aims to save the on-view channel IP address in the local Set-Top Box (STB), where STB acts as the front device for IPTV applications. The IP address, including the monitoring frequencies and time observed, is stored in the local STB. This facilitates the parents to monitor their wards, and misuse of the internet is also minimized. The logic block added to the onboard increases the chip area by 3.97m3, and the delay is 847 ps.

Towards real-time secured IP camera via state machine architecture

Towards real-time secured IP camera via state machine architecture

Operating room black box: Scrutinizer of theatre practices

ObjectiveAdverse surgical events are a major cause of morbidity, mortality, and disability worldwide. The cause of many such events can be attributed to interruptions in the operating room (OR), multitasking by surgeons, etc. The objective of this study was to observe the types and frequency of intraoperative workflow interruptions in our ORs. MethodThis cross-sectional study was conducted from March to April of 2023. An observational approach using an audio-video recording device was employed to record OR flow disruptions. One elective OR and one emergency OR under the Department of General Surgery were selected for the study. All open and laparoscopic surgeries conducted in the selected ORs were included. An Internet Protocol camera was installed in the selected ORs with a view of the entire room, including the anesthesia station. Audio-video recording was started after the first incision and stopped after closure of the surgical site. ResultOf the 51 cases that were studied, 45 (88.2%) were elective, and 18 (35.3%) were laparoscopic cases. They could be classified into 8 types of open procedures and 4 types of laparoscopic procedures. The mean maximum headcount inside the OR was 15.5 ± 3.6 and doors opened on average of 15.8 ± 6.0 times during a procedure. Other interruptions were surgeons attending phone calls (24, 47.1%), leaving the sterile area (21, 41.2%), technical disturbances (32, 62.7%), anesthetic interruptions (18, 35.3%), and faulty instruments (29, 56.9%). Elective procedures had a significantly higher average number of interruptions per operating hour than emergency procedures (17.5 ± 8.6 vs. 7.1 ± 2.9, p < 0.01). ConclusionPreventable factors such as faulty instruments, anesthetic interruption, and attending phone calls by the surgeon are commonly observed in ORs. They need to be addressed by timely surgical audits or the adoption of continued surveillance methods that can help take measures to minimize their occurrence.

Distributed multi-target tracking and active perception with mobile camera networks

Smart cameras are an essential component in surveillance and monitoring applications, and they have been typically deployed in networks of fixed camera locations. The addition of mobile cameras, mounted on robots, can overcome some of the limitations of static networks such as blind spots or back-lightning, allowing the system to gather the best information at each time by active positioning. This work presents a hybrid camera system, with static and mobile cameras, where all the cameras collaborate to observe people moving freely in the environment and efficiently visualize certain attributes from each person. Our solution combines a multi-camera distributed tracking system, to localize with precision all the people, with a control scheme that moves the mobile cameras to the best viewpoints for a specific classification task. The main contribution of this paper is a novel framework that exploits the synergies that result from the cooperation of the tracking and the control modules, obtaining a system closer to the real-world application and capable of high-level scene understanding. The static camera network provides global awareness of the control scheme to move the robots. In exchange, the mobile cameras onboard the robots provide enhanced information about the people on the scene. We perform a thorough analysis of the people monitoring application performance under different conditions thanks to the use of a photo-realistic simulation environment. Our experiments demonstrate the benefits of collaborative mobile cameras with respect to static or individual camera setups.

Coverage optimization of camera network for continuous deformable object

Coverage optimization of camera network for continuous deformable object

Driver Safety System

Abstract: Blind spots in vehicles and driver drowsiness are significant safety concerns that contribute to road accidents. To address these issues, we propose a comprehensive driver drowsiness and blind spot detection system using advanced technologies and image processing algorithms. The blind spot detection system employs ultrasonic sensors and an Arduino microcontroller board to gather real-time information about potential collision objects in the blind spot area. When a risk is detected, an LED alarm alerts the driver, enhancing their awareness and reducing the likelihood of accidents. Additionally, we explore the implementation of a camera-based blind spot detection system using deep learning techniques, offering a viable option for autonomous vehicles. For driver drowsiness detection, we present a non-intrusive method using a camera to continuously monitor the driver's facial features, such as eye and head movements. By analyzing these features, the system can accurately identify three driver states: awake, drowsy, and sleeping. When drowsiness is detected, the system activates alerts, such as visual and auditory cues, to awaken the driver and ensure their safety. The literature review greatly impacted our system's design, facilitating informed decisions and integration of cutting-edge technologies for an effective driver drowsiness and blind spot detection solution. The research paper also discusses the integration of these systems into various platforms, including mobile apps, Python programs, and IP cameras, providing flexible and cost-effective solutions for different vehicle types. Results from prototype implementations demonstrate the effectiveness and reliability of the proposed methods in detecting blind spots and drowsiness. Overall, this research enhances road safety via efficient blind spot &amp; drowsiness detection, saving lives by addressing driver fatigue. Utilizing tech &amp; machine learning, proposed systems advance active safety &amp; driver assistance

Face recognition of remote monitoring under the Ipv6 protocol technology of Internet of Things architecture

Abstract With the advent of the Internet of Things (IoT) era, the application of intelligent devices in the network is becoming more and more extensive, and the monitoring technology is gradually developing towards the direction of intelligence and digitization. As a hot topic in the field of computer vision, face recognition faces problems such as low level of intelligence and long processing time. Therefore, under the technical support of the IoTs, the research uses internet protocol cameras to collect face information, improves the principal component analysis (PCA), poses a PLV algorithm, and then applies it to the face recognition system for remote monitoring. The outcomes demonstrate that in the Olivetti Research Laboratory face database, the accuracy of PLV is relatively stable, and the highest and lowest are 98 and 94%, respectively. In Yale testing, the accuracy of this algorithm is 12% higher than that of PCA algorithm; In the database of Georgia Institute of Technology (GT), the PLV algorithm requires a time range of 0.2–0.3 seconds and has high operational efficiency. In the selected remote monitoring face database, the accuracy of the method is stable at more than 90%, with the highest being 98%, indicating that it can effectively improve the accuracy of face recognition and provide a reference technical means for further optimization of the remote monitoring system.

Timeline Binder: A Caption-based Approach for Creating Semantic Video Timeline

Video is an important type of multimedia which has myriad characteristics such as richer content and little prior structure. Major applications such as surveillance, medicine, education, entertainment, and sports heavily use videos. Internet protocol video is dominating the World’s Internet traffic. Large number of videos are made and uploaded daily. Looking for topics of one’s interest in these videos is a cumbersome task. Mostly, people are only interested in small portions of videos as opposed to the whole video. It is better to look for small but related segments of interest. Our proposed system Timeline Binder is an attempt to tackle this problem. For Timeline Binder, we have created our own dataset of online videos with subtitles. Based on those subtitles’ files, a concept vector is obtained. If a concept vector has a match for a user’s query tag, then timed segments for all related videos to that matched concept in the concept vector are combined into a single timeline thus making all related segments into a single video. In this research, we present performance measurements between Timeline Binder and other available applications such as Google2SRT and NR8. The results show that Timeline Binder performs better than Google2SRT and NR8.

A new framework for deep learning video based Human Action Recognition on the edge

Nowadays, video surveillance systems are commonly found in most public and private spaces. These systems typically consist of a network of cameras that feed into a central node. However, the processing aspect is evolving towards distributed approaches, leveraging edge-computing. These distributed systems are capable of effectively addressing the detection of people or events at each individual node. Most of these systems, rely on the use of deep-learning and segmentation algorithms which enable them to achieve high performance, but usually with a significant computational cost, hindering real-time execution. This paper presents an approach for people detection and action recognition in the wild, optimized for running on the edge, and that is able to work in real-time, in an embedded platform. Human Action Recognition (HAR) is performed by using a Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM). The input to the LSTM is an ad-hoc, lightweight feature vector obtained from the bounding box of each detected person in the video surveillance image. The resulting system is highly portable and easily scalable, providing a powerful tool for real-world video surveillance applications (in the wild and real-time action recognition). The proposal has been exhaustively evaluated and compared against other state-of-the-art (SOTA) proposals in five datasets, including four widely used (KTH, WEIZMAN, WVU, IXMAX) and a novel one (GBA) recorded in the wild, that includes several people performing different actions simultaneously. The obtained results validate the proposal, since it achieves SOTA accuracy within a much more complicated video surveillance real scenario, and using a lightweight embedded hardware.

IP Cameras Beyond Security: A Systematic Literature Review Examining Their Adoption and Potential in the Healthcare Industry

There is a growing interest of IP Camera usage in the healthcare industries. IP Cameras are now being used in healthcare including patient monitoring, healthcare, telemedicine, etc. This paper aims to determine the applications of the IP Camera in the healthcare industry including how it is used in these applications to serve as a tool for future researchers to understand how they can use IP cameras in their future endeavors. A total of 31 related literatures and studies published from 2019 to 2022 were gathered, all of which used IP cameras in their specific studies. A code for each study was created according to their specific application and use. Results show that IP cameras were mainly used in 12 applications, mostly for Deep Learning, Robotics, and COVID-19 Application. These cameras were also used in 7 roles, mainly remote monitoring, telemedicine, telerehabilitation, and capturing data for model training, testing, and use. Overall, this study showed that IP cameras play a crucial role in the healthcare industry and have the potential to improve and revolutionize the way healthcare services are delivered such as improving telemedicine and promoting the use of telerehabilitation. Future research includes examining the use of IP cameras in other industries, especially in smart home environments, how IP cameras are used in different deep learning algorithms, and analyzing the benefits and challenges of IP cameras in different applications. CCS CONCEPTS: CCS CONCEPTS -&gt; General and reference ~ Document types ~ General literature ACM Reference Format: This block will be automatically generated when manuscripts are processed after acceptance.

Exploitation Techniques of IoST Vulnerabilities in Air-Gapped Networks and Security Measures—A Systematic Review

IP cameras and digital video recorders, as part of the Internet of Surveillance Things (IoST) technology, can sometimes allow unauthenticated access to the video feed or management dashboard. These vulnerabilities may result from weak APIs, misconfigurations, or hidden firmware backdoors. What is particularly concerning is that these vulnerabilities can stay unnoticed for extended periods, spanning weeks, months, or even years, until a malicious attacker decides to exploit them. The response actions in case of identifying the vulnerability, such as updating software and firmware for millions of IoST devices, might be challenging and time-consuming. Implementing an air-gapped video surveillance network, which is isolated from the internet and external access, can reduce the cybersecurity threats associated with internet-connected IoST devices. However, such networks can also be susceptible to other threats and attacks, which need to be explored and analyzed. In this work, we perform a systematic literature review on the current state of research and use cases related to compromising and protecting cameras in logical and physical air-gapped networks. We provide a network diagram for each mode of exploitation, discuss the vulnerabilities that could result in a successful attack, demonstrate the potential impacts on organizations in the event of IoST compromise, and outline the security measures and mechanisms that can be deployed to mitigate these security risks.

Developing Motion Detection Approach through Image Processing using Kalman Filtering and Gaussian Mixture Model

Motion tracking through image processing requires validating the subject's presence which can use for automated systems. However, detecting a moving subject in a real-time video is a complex approach in terms of visual scenes. The Gaussian Mixture Model (GMM) is a flexible tool for image processing modeling. The concept of Kalman Filtering can be applied for noise filtering of a real-time video and tracking the motion in the image. The algorithms used in this study are to develop a motion-tracking system that uses a bounding box to assign tracks to the detected motion. The algorithm developed in MATLAB software includes the parameters for the Kalman Filtering and GMM. The testing involved an IP camera, Arduino Uno as the indicator if motion is detected, and different values of the parameters for the training frame. The confusion matrix is used for the statistical analysis of the five trials to detect the True Positive and measure the system's accuracy. The study showed promising results as it obtained a true positive rate of 0.6000 from trial 4, having the applied parameters of 3 for the gaussian number, 110 for the training frame, and 0.4 for the minimum background ratio.

Object Detection and Distance Measurement Algorithm for Collision Avoidance of Precast Concrete Installation during Crane Lifting Process

In the construction industry, the process of carrying heavy loads from one location to another by means of a crane is inevitable. This reliance on cranes to carry heavy loads is more obvious when it comes to high-rise building construction. Depending on the conditions and requirements on-site, various types of construction lifting equipment (i.e., cranes) are being used. As off-site construction (OSC) is gaining more traction recently, cranes are becoming more important throughout the construction project as precast concrete (PC) members are major components of OSC calling for lifting work. As a result of the increased use of cranes on construction sites, concerns about construction safety as well as the effectiveness of existing load collision prevention systems are attracting more attention from various parties involved. Besides the inherent risks associated with heavy load lifting, the unpredictable movement of on-site workers around the crane operation area, along with the presence of blind spots that obstruct the crane operator’s field-of-view (FOV), further increase the accident probability during crane operation. As such, the need for a more reliable and improved collision avoidance system that prevents lifted loads from hitting other structures and workers is paramount. This study introduces the application of deep learning-based object detection and distance measurement sensors integrated in a complementary way to achieve the stated need. Specifically, the object detection technique was used with the application of an Internet Protocol (IP) camera to detect the workers within the crane operation radius, whereas ultrasonic sensors were used to measure the distance of surrounding obstacles. Both applications were designed to work concurrently so as to prevent potential collisions during crane lifting operations. The field testing and evaluation of the integrated system showed promising results.

Vision-based multi-point real-time monitoring of dynamic displacement of large-span cable-stayed bridges

The existing vision measurement methods face a significant challenge in the multi-point displacement monitoring of large-span bridges due to the contradiction between the measurement scale and image spatial resolution. Based on a cable-stayed bridge on a kilometer scale, a new vision-based multi-point displacement monitoring system is proposed. The bridge was designed and arranged with a camera network, consisting of a series of double-head camera stations that were connected by cooperative markers. Based on the observed multi-point deflection and lateral displacement, the deformation evolution of the bridge in the two dimensions of time and space is discussed. The results show that the proposed vision-based camera network can accurately monitor the dynamic deflection and lateral displacement across the whole span of the bridge. The measurement errors induced by the camera motion can be well compensated based on the optical constraints of the proposed camera network. The feasibility of the vision method for long-span bridge monitoring has greatly improved. The proposed system can provide a new method for displacement monitoring and fine parameters for the safety evaluation of large-span bridges.

High-Throughput Plant Phenotyping System Using a Low-Cost Camera Network for Plant Factory

Plant phenotyping has been widely studied as an effective and powerful tool for analyzing crop status and growth. However, the traditional phenotyping (i.e., manual) is time-consuming and laborious, and the various types of growing structures and limited room for systems hinder phenotyping on a large and high-throughput scale. In this study, a low-cost high-throughput phenotyping system that can be flexibly applied to diverse structures of growing beds with reliable spatial–temporal continuities was developed. The phenotyping system was composed of a low-cost phenotype sensor network with an integrated Raspberry Pi board and camera module. With the distributed camera sensors, the system can provide crop imagery information over the entire growing bed in real time. Furthermore, the modularized image-processing architecture supports the investigation of several phenotypic indices. The feasibility of the system was evaluated for Batavia lettuce grown under different light periods in a container-type plant factory. For the growing lettuces under different light periods, crop characteristics such as fresh weight, leaf length, leaf width, and leaf number were manually measured and compared with the phenotypic indices from the system. From the results, the system showed varying phenotypic features of lettuce for the entire growing period. In addition, the varied growth curves according to the different positions and light conditions confirmed that the developed system has potential to achieve many plant phenotypic scenarios at low cost and with spatial versatility. As such, it serves as a valuable development tool for researchers and cultivators interested in phenotyping.

Deflection monitoring of immersed tunnel element during floating transportation and installation based on series camera network

The deflection of immersed tunnel element is an important indicator during floating transportation and installation phases. However, there is no strictly stable platform to install camera sensors while the element is in the process of transportation and installation, which poses a challenge to the application of vision-based methods. In this study, a novel vision measurement system based on the series camera network (SCN) with displacement-relay characteristics is proposed. The SCN is composed of multiple camera stations and multiple measuring points in series through the optical path. Lateral and vertical displacement-relay measurement methods are proposed to compensate for the self-motion of the camera station. For field testing, three camera stations and ten measuring points were installed at the E15 element of Shenzhen-Zhongshan Link, China. The results show that the measurement accuracy of the SCN can reach sub-millimeter level after compensating for the self-motion of the camera station. Subsequently, the SCN has been successfully applied to the deflection monitoring of E15 immersed tunnel element, and the deflection curves of the element during floating transportation and installation have been recorded for the first time. The proposed novel vision measurement system improves the automation of the immersed tunnel during construction.

Rapid provision of maps and volcanological parameters: quantification of the 2021 Etna volcano lava flows through the integration of multiple remote sensing techniques

At active volcanoes recurring eruptive events, erosive processes and collapses modify the edifice morphology and impact monitoring and hazard mitigation. At Etna volcano (Italy) between February and October 2021, 57 paroxysmal events occurred from the South-East Crater (SEC), which is currently its most active summit crater. Strombolian activity and high lava fountains (up to 4 km) fed lava flows towards the east, south and south-west, and caused fallout of ballistics (greater than 1 m in diameter) within 1–2 km from the SEC. The impacted area does not include permanent infrastructure, but it is visited by thousands of tourists. Hence, we rapidly mapped each lava flow before deposits became covered by the next event, for hazard mitigation. The high frequency of the SEC paroxysms necessitated integration of data from three remote sensing platforms with different spatial resolutions. Satellite (Sentinel-2 MultiSpectral Instrument, PlanetScope, Skysat and Landsat-8 Operational Land Imager) and drone images (visible and thermal) were processed and integrated to extract digital surface models and orthomosaics. Thermal images acquired by a permanent network of cameras of the Istituto Nazionale di Geofisica e Vulcanologia were orthorectified using the latest available digital surface model. This multi-sensor analysis allowed compilation of a geodatabase reporting the main geometrical parameters for each lava flow. A posteriori analysis allowed quantification of bulk volumes for the lava flows and the SEC changes and of the dense rock equivalent volume of erupted magma. The analysis of drone-derived digital surface models enabled assessment of the ballistics’ distribution. The developed methodology enabled rapidly and accurate characterisation of frequently occurring effusive events for near real-time risk assessment and hazard communication.

A Coalition Formation Approach for Privacy and Energy-Aware Split Deep Learning Inference in Edge Camera Network

Recently, an increasing amount of application tasks have depended on deep learning (DL) inference models on Internet of Things (IoT) based camera networks. However, it is challenging to perform the inference of such resource-hungry DL models on the computationally limited IoT system. Compared to cloud computing, edge computing deploys resources near the end-users to reduce the transmission delay and retains the raw data on the trusted servers to mitigate privacy concerns. Since resources at the edge are limited, management like user association decisions, edge resources allocation, and device configuration with DL model parameter selection becomes essential. This work proposes a coalition formation game-based algorithm to solve the association problem between IoT-based cameras and the edge nodes. Our goal is to maximize the social welfare that consists of multi-view detection enhancement, the privacy retained preference, and the power savings from the cameras. Besides, we adopt the concept of split-ML to provide more flexibility for networking and computing resources allocation at the edge. The final coalition structure is proved to converge and maintain stability. The simulation results show that each design knob, including association decisions made by coalition formation, DNN layer-level partition, and multi-view detection, is essential under different scenario settings.

Approach in installing and configuring general program monitoring client devise for IPTV monitoring of A1 and Vivacom

In the article, we address the issue of automated initialization, installation, and configuration of the General Program Monitoring, version 3 application with minimal human involvement and manipulation. General Program Monitoring is Internet Protocol TV monitoring software built from scratch. It is used to monitor the viewing behaviour of customers of the television operators A1 and Vivacom. The purpose of the article is to present an approach for an automated installation and configuration process. To achieve this goal, we review the design and implementation of the software as well as the client application. The novelty of the approach is realized in a fully automated integration of the monitoring software and the procedures for its installation and configuration on the client device and the network, in the conditions of independence from the architecture of the network infrastructure and other accompanying hardware and software limitations.

Identification of Changing Personnel with Double-Layer Network Fusion and Bi-Level Monitoring Mechanism

Person re-identification (Re-ID) is an essential part of visual surveillance that aims to identify and locate persons from multiple network cameras without conflicting viewpoints. Although significant advances have been made in recent years with the use of deep learning, there are still many challenges in vision such as occlusion, exposure, background clutter, misalignment, scale, perspective, low resolution and illumination, and cross-camera methods. Dressing redefinition is a hot topic in education right now. Most existing methods assume that people's clothes do not change in a short time, but they do not apply when people change clothes. Accordingly, this article introduces a double-layer garment changer re-identification network that integrates the secondary care process through clustering and fine-grained knowledge in space and training the garment classification branch to increase the sensitivity of the network to garment characteristics. In this method, auxiliary equipment such as human bone is not used and the complexity of the model is greatly reduced compared to other methods. This article runs experiments on the famous redefined PRCC data and large-scale long-term dataset (LaST). Experimental results show that the method in this article is superior to existing methods.