IPTV Research Articles

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

Traditional attendance recording methods can be replaced with artificial intelligence (AI) methods. This paper aims to develop an automated attendance system with a user-friendly graphical interface in Arabic to enhance user interaction. The Python programming language is used to build the system, which employs object detection and feature extraction algorithms. This system processes a live broadcast from an IP camera installed in the classroom. The system employs object detection algorithms to detect the faces in the video. The face locations are then sent to the feature extraction algorithms to extract and compare features with the features stored in the database. Our paper concludes that automating the attendance registration process using facial recognition technology can achieve up to 100% accuracy. Comparing our proposed system with previous studies, we find that it outperforms them in several aspects. Our method handles real-time tests with a larger number of students. It accommodates students positioned at different distances from the camera within the classroom, demonstrating our method's effectiveness in automating the attendance registration process.

Camera Network Topology Mapping Based on the Integration of Network Information and Physical Distribution Under the Background of Communication Security

At present, most cameras use internal networks and use methods such as Traceroute for security protection, which cannot meet the requirements of camera network mapping. Therefore, a camera mapping scheme of network information and physical distribution is proposed. Firstly, the network topology problem of video content information collection was analyzed. This paper uses the mapping relationship between network space and physical space to propose the subnet division conjecture method and complete the preliminary mapping of the network through video data screening. Considering the insufficient coverage of topology mapping, a judgment and inference method based on Bayesian classification technology and network information is proposed, and the results are corrected and evaluated through the test of Jackard coefficient. In the preliminary network topology performance test, two state-of-the-art schemes are selected for experimental comparison. When the number of nodes in the proposed scheme is 5, 25, and 50, the mapping can be completed in the shortest time, and the accuracy reaches 80%. However, the surveying and mapping accuracy of the proposed scheme in the preliminary test is low, and the network information method is used for data screening. In the final surveying and mapping performance test, when the number of nodes is 40, the accuracy of the proposed scheme is 96%, which is better than previously proposed schemes, while the testing delay time is shorter. The technology proposed in the study has the best overall performance. It can effectively solve the problem of intranet surveying and mapping and has important reference value for the security protection of the camera network.

A DASH server-side delay-based representation switching solution to improve the quality of experience for low-latency live video streaming

This work addresses the integration of real-time transmission systems, including IP cameras and production systems (like OBS or vMix), that use protocols such as RTSP (Real Time Streaming Protocol) or SRT (Secure Reliable Transport), with content distribution technology based on LL-DASH (Low Latency DASH -Dynamic Adaptive Streaming over HTTP-), taking advantage of the fact that DASH offers significant well-known advantages for content distribution over the Internet and via CDNs (Content Delivery Networks). Considering the limitations of the LL-DASH standard regarding the adaptation to network conditions, this paper proposes a new solution called Server-Side Representation Switching (SSRS). SSRS uses an approach based on the server measuring the delay in the requests made by clients, whose variation may be due to a decrease in bandwidth, as occurs in Wi-Fi networks with a high number of clients. To evaluate the effectiveness of the proposed solution, a testbed has been developed that allows the performance evaluation of both the LL-DASH system and the solution based on server-side decision-making. In addition, the developed solution has been compared with known algorithms (L2A and LoL+) integrated into the Dash.js player. The results show that the Server-Side Representation Switching solution offers a good trade-off between the transmitted quality and the final delay measured at the client, compared to the other algorithms evaluated. Moreover, it holds the advantage of being straightforward to implement and does not require any modifications to the players used.• Networks • Network performance evaluation • Network performance analysis.

Multi-Objective Optimal Deployment of Road Traffic Monitoring Cameras: A Case Study in Wujiang, China

This study presents a multi-objective optimal framework for deploying traffic monitoring cameras at road networks. Compared with previous studies that focused on addressing single traffic problem such as OD estimation, link flow observation, path flow reconstruction, and travel time estimation, this study aims to address a comprehensive traffic management problem, including crash prevention, traffic violation governance, and traffic efficiency improvement. First, a potential principle for selecting the location of traffic monitoring deployment is determined, taking into account the key signalized intersections, areas prone to traffic congestion, crash-prone spots, and areas prone to traffic violations. Then, a multi-objective optimal model is developed to minimize the ATFM (i.e., average traffic volume of each five minutes), TCF (i.e., traffic crash frequency), and TVF (i.e., traffic violation frequency) while adhering to cost constraints. Finally, RVEA and NSGA-II algorithms are used to solve the multi-objective optimal model, respectively, and a comprehensive metric is proposed to evaluate the deployment schemes. The case study results demonstrate that the solutions obtained by the RVEA algorithm outperform those of the NSGA-II algorithm, and the best traffic monitoring deployment rate is 62.79%, under cost constraints. In addition, the comparison using the FAHP method also illustrates that the RVEA scheme is superior to the NSGA-II scheme. The above research results could potentially be used to optimize the locations of traffic cameras in road networks, which help to improve traffic management.

Parking Space Finder Using Image Processing and Machine Learning

Finding a parking spot has become difficult in many places all over the globe due to the rise in the number of vehicles on the road. The use of image processing and machine learning methods to count the number of accessible parking spaces in a particular location is one of many smart parking solutions that have been put forth to handle this problem. In this study, we show a clever parking system that counts the number of parking spots that are open in real-time by using machine learning and image processing methods to identify and discover available spaces. The suggested system consists of a camera network that records pictures of the parking area and an algorithm that analyses these images to find open parking spots. To extricate the parking spots from the recorded pictures, the image processing algorithm carries out a number of image enhancement and segmentation operations. The machine learning programe then examines these segmented pictures to categories each parking spot as either empty or filled. For the ease of users, the system offers a real-time count of the available parking spots, which can be shown on electronic signage or sent to a smartphone application. The suggested clever parking system has a number of benefits over conventional parking systems. It can increase the effectiveness of parking administration, lessen traffic congestion, and make parking easier for travelers. In order to improve the total city infrastructure, it can also be combined with other smart city systems like traffic management, public transit, and environmental tracking systems

Toward Improved Real‐Time Rainfall Intensity Estimation Using Video Surveillance Cameras

AbstractUnder global climate change, urban flooding occurs frequently, leading to huge economic losses and human casualties. Extreme rainfall is one of the direct and key causes of urban flooding, and accurate rainfall estimates at high spatiotemporal resolution are of great significance for real‐time urban flood forecasting. Using existing rainfall intensity measurement technologies, including ground rainfall gauges, ground‐based radar, and satellite remote sensing, it is challenging to obtain estimates of the desired quality and resolution. However, an approach based on processing distributed surveillance camera network imagery through machine learning algorithms to estimate rainfall intensities shows considerable promise. Here, we present a novel approach that first extracts raindrop information from the surveillance camera images (rather than using the raw imagery directly), followed by the use of convolutional neural networks to estimate rainfall intensity from the resulting raindrop information. Evaluation of the approach on 12 rainfall events under both daytime and nighttime conditions shows that generalization ability, and especially nighttime predictive performance, is significantly improved. This represents an important step toward achieving real‐time, high spatiotemporal resolution, measurement of urban rainfall at relatively low cost.

A deep-learning framework running on edge devices for handgun and knife detection from indoor video-surveillance cameras

The early detection of handguns and knives from surveillance videos is crucial to enhance people’s safety. Despite the increasing development of Deep Learning (DL) methods for general object detection, weapon detection from surveillance videos still presents open challenges. Among these, the most significant are: (i) the very small size of the weapons with respect to the camera field of view and (ii) the need of a real-time feedback, even when using low-cost edge devices for computation. Complex and recently-developed DL architectures could mitigate the former challenge but do not satisfy the latter one. To tackle such limitation, the proposed work addresses the weapon-detection task from an edge perspective. A double-step DL approach was developed and evaluated against other state-of-the-art methods on a custom indoor surveillance dataset. The approach is based on a first Convolutional Neural Network (CNN) for people detection which guides a second CNN to identify handguns and knives. To evaluate the performance in a real-world indoor environment, the approach was deployed on a NVIDIA Jetson Nano edge device which was connected to an IP camera. The system achieved near real-time performance without relying on expensive hardware. The results in terms of both COCO Average Precision (AP = 79.30) and Frames per Second (FPS = 5.10) on the low-power NVIDIA Jetson Nano pointed out the goodness of the proposed approach compared with the others, encouraging the spread of automated video surveillance systems affordable to everyone.

Spooling Device Optimization with IP Camera and Active Infrared Sensor

Spooling Device Optimization with IP Camera and Active Infrared Sensor

Research on a measurement method for spatio-temporal distribution of fragments based on a high-speed camera network

To reliably measure multiple spatio-temporal distribution parameters of fragments in the blast field, a measurement method for spatio-temporal distribution of fragments based on a high-speed camera network is proposed. The method is based on the principle of binocular stereo vision measurement and consists of four steps: fragment image acquisition, target detection of multi-view images, fitting of motion trajectory, and calculation of the spatio-temporal distribution parameters. The test results demonstrate that the method can simultaneously measure multiple parameters in the intersecting field of view. Compared with the measurement results of the array target, the absolute value of the measurement relative error of velocity was less than 1.5%, and the measurement results of scatter angle and distribution density were consistent. It indicates that the measurement results of the proposed method are reliable, and the method is a feasible and effective measurement method for spatio-temporal distribution parameters of fragments in the explosion filed.

Development of a Smart Hospital Bed Based on Deep Learning to Monitor Patient Conditions

An Internet of Things-based automated patient condition monitoring and detection system is discussed and built in this work. The proposed algorithm that underpins the smart-bed system is based on deep learning. The movement and posture of the patient’s body may be determined with the help of wearable sensor-based devices. In this work, an internet protocol camera device is used for monitoring the smart bed, and sensor data from five key points of the smart bed are core components of our approach. The Mask Region Convolutional Neural Network approach is used to extract data from many important areas from the body of the patient by collecting data from sensors. The distance and the time threshold are used to identify motions as being either connected with normal circumstances or uncomfortable ones. The information from these key locations is also utilised to establish the postures in which the patient is lying in while they are being treated on the bed. The patient’s body motion and bodily expression are constantly monitored for any discomfort if present. The results of the experiments demonstrate that the suggested system is valuable since it achieves a true-positive rate of 95% while only yielding a false-positive rate of 4%.

Divergent or convergent: how do forest carnivores use time in the Greater Yellowstone Ecosystem?

Abstract Divergent activity can change the intensity of species interactions, largely affecting species distributions and abundances, and consequently influencing the composition and function of ecological communities. Few assessments of activity patterns have focused questions around different resource constraints or have examined varying time frames when interaction strengths are expected to increase. We evaluated how activity among carnivores and their prey shifted from early to late winter, coinciding with a presumed decrease in food resources for carnivores, and we measured time between species detections within a camera station. Our study species were three forest carnivores—Pacific martens (Martes caurina), Rocky Mountain red foxes (Vulpes vulpes macroura), coyotes (Canis latrans); and two of their prey—American red squirrels (Tamiasciurus hudsonicus), and snowshoe hares (Lepus americanus). We sampled these species across an extensive network of cameras (n = 107) during the 2014–2017 winter seasons in the Greater Yellowstone Ecosystem, Wyoming. We generated kernel density plots for timing of photographs and calculated the coefficient of overlap among density plots for our predators and prey during early and late winter. Furthermore, we calculated the time-between-detections (i.e., hours) among forest carnivores. We found no consistent trends in time-between-detections across our species pairs. Pacific martens exhibited cathemeral activity that aligned with the peaks in activity of the two prey species. Temporal overlap between coyote and red fox activities was small in early winter, whereas coyotes modified activity in late winter such that they more closely aligned with red foxes. This intraguild convergence of activity may reflect an increase in resource constraints and have consequences for competitive interactions between these two canids. Our study supports the notion that variation in time is an important axis in facilitating coexistence among these forest carnivores and prey species.

A novel high-precision photogrammetric technique for monitoring subway tunnel deformation

This paper presents a low cost and high precision tunnel deformation monitoring system based on network camera photogrammetry. The system uses digital image technology to analyze and process the reflective mark of the tunnel structure monitoring section in real time, enabling real-time monitoring and early warning of deformations in the tunnel structure. This paper focuses on how to effectively improve the monitoring accuracy of image analysis technology in photogrammetric system, specifically by achieving a deformation accuracy of 0.1 mm through the use of images with a resolution of 1 mm/pix. The robustness of the analysis algorithm and methods of optimizing and reducing the computational efforts of the algorithm for real-time analysis are also discussed. The effectiveness of these methods is verified through the deformation monitoring of the Tianjin subway tunnel.

Automatic Speaker Positioning in Meetings Based on YOLO and TDOA.

In recent years, many things have been held via video conferences due to the impact of the COVID-19 epidemic around the world. A webcam will be used in conjunction with a computer and the Internet. However, the network camera cannot automatically turn and cannot lock the screen to the speaker. Therefore, this study uses the objection detector YOLO to capture the upper body of all people on the screen and judge whether each person opens or closes their mouth. At the same time, the Time Difference of Arrival (TDOA) is used to detect the angle of the sound source. Finally, the person's position obtained by YOLO is reversed to the person's position in the spatial coordinates through the distance between the person and the camera. Then, the spatial coordinates are used to calculate the angle between the person and the camera through inverse trigonometric functions. Finally, the angle obtained by the camera, and the angle of the sound source obtained by the microphone array, are matched for positioning. The experimental results show that the recall rate of positioning through YOLOX-Tiny reached 85.2%, and the recall rate of TDOA alone reached 88%. Integrating YOLOX-Tiny and TDOA for positioning, the recall rate reached 86.7%, the precision rate reached 100%, and the accuracy reached 94.5%. Therefore, the method proposed in this study can locate the speaker, and it has a better effect than using only one source.

Deployment Optimization of Spotlights for Visual Coverage in Pure Dark Indoor Scene

In this paper, a deployment method of spotlights is studied for visual coverage task in pure dark indoor scene. To characterize the illumination performance of target in visual sensor, a criterion called illumination strength of spotlight with combining the diffuse reflection and specular reflection on the basis of distance/angle illumination attenuation of light source as well as shading is proposed. Then, an optimization problem is formulated for a number of spotlights with the aim of covering the target with a desired luminance value in camera network as uniformly as possible. An elimination genetic algorithm possessing faster convergence rate and better convergence performance than standard genetic algorithm is utilized to solve the optimization problem. Finally, numerical simulations and experiments are conducted to validate the proposed deployment method of multiple spotlights for visual coverage task in pure dark indoor scene.

Determining the Feasibility of Applying Existing Criteria for Solving Operational Problems in the Design of CCTV Information Systems

Comparative analysis of the criteria for designing video surveillance information systems was conducted based on international and national CCTV standards. Their greatest differences concern identification and recognition tasks, and the smallest -– monitoring and detection. A comprehensive analysis of the impact of the technical characteristics of video cameras on the results of solving CCTV operational problems has not been carried out at this time. Therefore, the current direction of research is to determine the maximum feasibility of using the formed set of criteria. Increasing the resolution of video cameras leads to an increase in the number of pixels per meter of linear field of view. This in turn improves the display quality of the surveillance object at a greater distance from the camera. With the emergence of new information technologies, the resolution of matrices in video surveillance systems will increase. It will lead to qualitative and quantitative changes in the established set of criteria for designing of CCTV information systems. The methodology of estimation of spatial resolution of images of video surveillance information systems (CCTV) is presented in this work. The theoretical foundations for analyzing a set of technical characteristics of video cameras are confirmed by the results of computer modeling using specialized software "IP Video System Design Tool". The overall discrepancy between the calculated and modeled data does not exceed 3%. As a result, it was found that the use of a 25 MP video camera satisfies and exceeds all currently existing criteria for designing CCTV information systems for solving various types of operational tasks and the possibility of human vision. Therefore, with the cheapening and widespread use of video cameras with a resolution of over 25 MP, it will be necessary to review the feasibility of using existing criteria for designing CCTV information systems.

Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization

With the rapid development of science and technology, the living habits of people have also changed from those in the past; the diet, living environment, various life pressures, etc., all overwhelm the body and mind, meaning that, nowadays, more people are suffering from mental illness and cardiovascular disease year on year. Therefore, a non-contact measurement of heart rate and heart rate variability (HRV) is proposed to assist physicians in diagnosing symptoms related to mental illness and cardiovascular disease. In this paper, continuous images are obtained by general network cameras with non-contact, facial feature points and regions of interest (ROI) employed to track faces and regional images; HRV parameters were analyzed with the green wavelength of RGB color space. The artifact signal is eliminated by a hybrid algorithm of independent component analysis (ICA) and particle swarming optimization (PSO). Finally, the values of heart rate and HRV are obtained with signal processes of using band-pass filter, fast Fourier transform (FFT), and power spectrum analysis in the time and frequency domains, respectively. The non-contact measurement performance of the proposed method can effectively not only avoid infection doubts and obtain heart rate and HRV quickly, but also provide better physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE), than those of recent published papers.

Evaluating MODIS snow products using an extensive wildlife camera network

Snow covers a maximum of 47 million km2 of Earth's northern hemisphere each winter and is an important component of the planet's energy balance, hydrology cycles, and ecosystems. Monitoring regional and global snow cover has increased in urgency in recent years due to warming temperatures and declines in snow cover extent. Optical satellite instruments provide large-scale observations of snow cover, but cloud cover and dense forest canopy can reduce accuracy in mapping snow cover. Remote camera networks deployed for wildlife monitoring operate below cloud cover and in forests, representing a virtually untapped source of snow cover observations to supplement satellite observations. Using images from 1181 wildlife cameras deployed by the Norwegian Institute for Nature Research (NINA), we compared snow cover extracted from camera images to Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products during winter months of 2018–2020. Ordinal snow classifications (scale = 0–4) from cameras were closely related to normalized difference snow index (NDSI) values from the MODIS Terra Snow Cover Daily L3 Global 500 m (MOD10A1) Collection 6 product (R2 = 0.70). Tree canopy cover, the normalized difference vegetation index (NDVI), and image color mode influenced agreement between camera images and MOD10A1 NDSI values. For MOD10A1F, MOD10A1's corresponding cloud-gap filled product, agreement with cloud-gap filled values decreased from 78.5% to 56.4% in the first three days of cloudy periods and stabilized thereafter. Using our camera data as validation, we derived a threshold to create daily binary maps of snow cover from the MOD10A1 product. The threshold corresponding to snow presence was an NDSI value of 40.50, which closely matched a previously defined global binary threshold of 40 using the MOD10A2 8-day product. These analyses demonstrate the utility of camera trap networks for validation of snow cover products from satellite remote sensing, as well as their potential to identify sources of inaccuracy.

Real-time batch processing at a GPU-based edge with a passive optical network

In recent years, advances in deep learning technology have significantly improved the research and services relating to artificial intelligence. Real-time object recognition is an important technique in smart cities, in which low-cost network deployment and low-latency data transfer are key technologies. In this study, we focus on time- and wavelength-division multiplexed passive optical network (TWDM-PON)-based inference systems to deploy cost-efficient networks that accommodate several network cameras. A significant issue for a graphics processing unit (GPU)-based inference system via a TWDM-PON is the optimal allocation of the upstream wavelength and bandwidth to enable real-time inference. However, an increase in the batch size of the arrival data at the edge servers, thereby ensuring low-latency transmission, has not been considered previously. Therefore, this study proposes the concept of an inference system in which a large number of cameras periodically upload image data to a GPU-based server via the TWDM-PON. Moreover, we propose a cooperative wavelength and bandwidth allocation algorithm to ensure low-latency and time-synchronized data arrivals at the edge. The performance of the proposed scheme is verified through a computer simulation.

Volcanic plume height monitoring using calibrated web cameras at the Icelandic Meteorological Office: system overview and first application during the 2021 Fagradalsfjall eruption

The Icelandic Meteorological Office maintains a national network of webcams designed and built in house for environmental monitoring. During the 2021 Fagradalsfjall eruption these cameras, along with a temporary near-field network of commercial cameras installed by the Department of Civil Protection and Emergency Management, were used to estimate the height of the SO_2 plume and lava fountain. Here we present the webcam designs, the techniques used to calibrate them, and the messaging system and web interface that allow near real time measurements to be made from the images. With this system we were able to make estimates of heights with an accuracy on the order of tens to a few hundreds of meters with a lag typically of five to ten minutes at up to ten minute intervals when weather conditions were favorable. The plume heights were then used to constrain the performance of the SO_2 dispersion model used for air quality forecasts while fountain heights were used to delineate danger zones where visitors at the eruption site were in danger of being hit by ballistic clasts.

METRIC—Multi-Eye to Robot Indoor Calibration Dataset

Multi-camera systems are an effective solution for perceiving large areas or complex scenarios with many occlusions. In such a setup, an accurate camera network calibration is crucial in order to localize scene elements with respect to a single reference frame shared by all the viewpoints of the network. This is particularly important in applications such as object detection and people tracking. Multi-camera calibration is a critical requirement also in several robotics scenarios, particularly those involving a robotic workcell equipped with a manipulator surrounded by multiple sensors. Within this scenario, the robot-world hand-eye calibration is an additional crucial element for determining the exact position of each camera with respect to the robot, in order to provide information about the surrounding workspace directly to the manipulator. Despite the importance of the calibration process in the two scenarios outlined above, namely (i) a camera network, and (ii) a camera network with a robot, there is a lack of standard datasets available in the literature to evaluate and compare calibration methods. Moreover they are usually treated separately and tested on dedicated setups. In this paper, we propose a general standard dataset acquired in a robotic workcell where calibration methods can be evaluated in two use cases: camera network calibration and robot-world hand-eye calibration. The Multi-Eye To Robot Indoor Calibration (METRIC) dataset consists of over 10,000 synthetic and real images of ChAruCo and checkerboard patterns, each one rigidly attached to the robot end-effector, which was moved in front of four cameras surrounding the manipulator from different viewpoints during the image acquisition. The real images in the dataset includes several multi-view image sets captured by three different types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455 and Intel RealSense Lidar L515, to evaluate their advantages and disadvantages for calibration. Furthermore, in order to accurately analyze the effect of camera-robot distance on calibration, we acquired a comprehensive synthetic dataset, with related ground truth, with three different camera network setups corresponding to three levels of calibration difficulty depending on the cell size. An additional contribution of this work is to provide a comprehensive evaluation of state-of-the-art calibration methods using our dataset, highlighting their strengths and weaknesses, in order to outline two benchmarks for the two aforementioned use cases.