Live Video Feed Research Articles

Intervention time and adverse events in a canadian epilepsy monitoring unit: An updated audit

BackgroundOptimizing patient safety in the epilepsy monitoring unit (EMU) has become a topic of increasing interest. We performed an audit of our center's new single-floor EMU, assessing intervention rate (IR), intervention time (IT), and adverse events (AEs). MethodsA prospective study was conducted on all clinical seizures of patients admitted over a one-year period at our Canadian academic tertiary care center's new single-floor EMU. This single-floor EMU was supervised by EEG technologists during daytime (similar to the old set-up) and beneficiary attendants during nighttime/weekends (versus live video feed to the central nursing station on the neurology ward previously). Among 153 admissions, 79 were analyzed, and a total of 537 seizures were reviewed to assess IR, IT, and AEs. Univariate comparisons were performed with our double-floor EMU, which we reported in a previous publication. ResultsIn our new single-floor EMU, the IR was 61.1 % and overall median IT was 29.0s (19.0s–45.9s). The AE rate was 4.8 %. Compared to previously reported numbers for our old double-floor EMU (IR = 27.8 %; IT = 21.0s; AE = 1.2 %), the IR was significantly higher ((p < 0.001) but unexpectedly, the median IT was higher (p < 0.001) as well as the AE rate (p < 0.001). ConclusionThis prospective evaluation revealed a small but non-negligible rate of complications in our EMU, higher than our prior retrospective audit. Heightened levels of supervision in our new single-floor EMU led to higher IR. This may have led to artificially longer ITs.

A machine learning-driven web application for sign language learning.

Addressing the increasing demand for accessible sign language learning tools, this paper introduces an innovative Machine Learning-Driven Web Application dedicated to Sign Language Learning. This web application represents a significant advancement in sign language education. Unlike traditional approaches, the application's unique methodology involves assigning users different words to spell. Users are tasked with signing each letter of the word, earning a point upon correctly signing the entire word. The paper delves into the development, features, and the machine learning framework underlying the application. Developed using HTML, CSS, JavaScript, and Flask, the web application seamlessly accesses the user's webcam for a live video feed, displaying the model's predictions on-screen to facilitate interactive practice sessions. The primary aim is to provide a learning platform for those who are not familiar with sign language, offering them the opportunity to acquire this essential skill and fostering inclusivity in the digital age.

AI Assistant for Visually Impaired

This paper presents an advanced assistive technol- ogy system aimed at improving accessibility and independence for visually impaired individuals. Utilizing artificial intelligence (AI) and computer vision techniques, the system provides real- time auditory feedback to users about their surroundings. The core components of the system include image captioning, face recognition, and depth estimation, all integrated to offer a comprehensive understanding of the environment. The system captures live video feed through a webcam, processes the images using pre-trained models like CLIPSeg for image segmentation and DPT for depth estimation, and generates textual descriptions of detected objects and their spatial distances. These descriptions are translated from English to Kannada using Google’s transla- tion services and converted into speech with the gTTS library, ensuring accessibility for Kannada-speaking users. Additionally, the system employs face recognition to identify known individuals in the vicinity, providing personalized auditory notifications. The combination of these technologies enables the system to offer context-aware assistance, helping visually impaired users to navigate and interact with their surroundings more effectively. Experimental results demonstrate the system’s capability to deliver accurate, real-time feedback, highlighting its potential to significantly enhance the quality of life for visually impaired individuals.

Advanced Tactical Helmet for Military Use

Abstract: Advanced features on a tactical helmet designed for military use. Soldiers in today's military operations must have improved situational awareness, strong communication skills, and superior protection when fighting. A new generation of tactical helmets with enhanced features is required because the standard helmets now in use do not adequately fulfil these criteria. To improve army performance and survivability The helmet has several extra features in addition to its extensive ESP32 CAM for real-time data visualization, communication systems for seamless inter-squad communication, and improved ballistic protection materials. Among these characteristics include the incorporation of a helmet camera's First-Person-View (FPV) live video feed into a base station, along with the base station's face detection capabilities for enhanced identification. SolidWorks was used in the design process to produce a unique 3D-printed helmet, while ESP32 and ESP8266 were used to power a custom mainboard that effectively controlled the helmet's functions. this creative solution aims to significantly improve military operations, increase survivability, and ensure the safety of soldiers on the battlefield.

Head-locked, world-locked, or conformal diminished-reality? An examination of different AR solutions for pedestrian safety in occluded scenarios

Many collisions between pedestrians and cars are caused by poor visibility, such as occlusion by a parked vehicle. Augmented reality (AR) could help to prevent this problem, but it is unknown to what extent the augmented information needs to be embedded into the world. In this virtual reality experiment with a head-mounted display (HMD), 28 participants were exposed to AR designs, in a scenario where a vehicle approached from behind a parked vehicle. The experimental conditions included a head-locked live video feed of the occluded region, meaning it was fixed in a specific location within the view of the HMD (VideoHead), a world-locked video feed displayed across the street (VideoStreet), and two conformal diminished reality designs: a see-through display on the occluding vehicle (VideoSeeThrough) and a solution where the occluding vehicle has been made semi-transparent (TransparentVehicle). A Baseline condition without augmented information served as a reference. Additionally, the VideoHead and VideoStreet conditions were each tested with and without the addition of a guiding arrow indicating the location of the approaching vehicle. Participants performed 42 trials, 6 per condition, during which they had to hold a key when they felt safe to cross. The keypress percentages and responses from additional questionnaires showed that the diminished-reality TransparentVehicle and VideoSeeThrough designs came out most favourably, while the VideoHead solution caused some discomfort and dissatisfaction. An analysis of head yaw angle showed that VideoHead and VideoStreet caused divided attention between the screen and the approaching vehicle. The use of guiding arrows did not contribute demonstrable added value. AR designs with a high level of local embeddedness are beneficial for addressing occlusion problems when crossing. However, the head-locked solutions should not be immediately dismissed because, according to the literature, such solutions can serve tasks where a salient warning or instruction is beneficial.

TeleAware Robot: Designing Awareness-augmented Telepresence Robot for Remote Collaborative Locomotion

Telepresence robots can be used to support users to navigate an environment remotely and share the visiting experience with their social partners. Although such systems allow users to see and hear the remote environment and communicate with their partners via live video feed, this does not provide enough awareness of the environment and their remote partner's activities. In this paper, we introduce an awareness framework for collaborative locomotion in scenarios of onsite and remote users visiting a place together. From an observational study of small groups of people visiting exhibitions, we derived four design goals for enhancing the environmental and social awareness between social partners, and developed a set of awareness-enhancing techniques to add to a standard telepresence robot - named TeleAware robot. Through a controlled experiment simulating a guided exhibition visiting task, TeleAware robot showed the ability to lower the workload, facilitate closer social proximity, and improve mutual awareness and social presence compared with the standard one. We discuss the impact of mobility and roles of local and remote users, and provide insights for the future design of awareness-enhancing telepresence robot systems that facilitate collaborative locomotion.

VR digital twin of office space with computer vision-based estimation of room occupancy and power consumption

AbstractIn the past years, energy consumption has increased rapidly due to many factors, including the rise in technology adoption. This has many downfalls, from higher costs to CO$$_2$$ 2 emissions. Human activities in offices and houses represent a considerable amount of energy usage. A digital twin (DT) of an open-plan common space is created, serving the purpose of remote room occupancy monitoring and automatic detection of energy consumption. A virtual reality (VR) model is developed and integrated to temperature, humidity and imaging sensors. For maintaining privacy, images are processed in local computers to measure occupancy levels and live video feed were never transmitted. The same set of imaging sensors were also used in a bespoke computer vision module for energy consumption estimation. The human avatars were mapped with high correlation (R$$^{2}$$ 2 $$= 0.85$$ = 0.85 ) with actual positions on floor. Our energy consumption algorithm accuracy obtained true positive rate of $$91.58\%$$ 91.58 % and F1 score of $$81.96\%$$ 81.96 % . Finally, all this information is transmitted and visualized to the 3D digital twin for remote monitoring and simulation.

Development of an IoT-based firefighting drone for enhanced safety and efficiency in fire suppression

An uncontrolled fire poses severe threats to both humans and the environment, making firefighting a perilous and complex task. Traditional fire suppression methods are inefficient, costly, and without thorough testing, leading to delays in gaining control over fire outbreaks. This paper presents a novel firefighting drone aimed at mitigating risks to firefighters by extinguishing fires and providing real-time imaging, gas concentration and fire location data monitoring. The proposed intelligent quadcopter utilizes the Pixhawk PX4 microcontroller for precise control and the Pixhawk Telemetry system for data processing. The proposed device is constructed from an ultra-strength S500 Quadcopter frame, NodeMCU, Arduino Nano, various gas sensors, a servo motor to extinguish the fire and a camera to detect fire events in real time. Equipped with an FPV camera and a video transmitter, it transmits live video feed to the ground, enabling efficient navigation using the Flysky I6X controller. The intended position and height of the drone are controlled using an adaptive optimization technique known as fuzzy-based backstepping control. This article demonstrates the effectiveness of the device by collecting and analyzing gas emissions data from controlled burns of various materials. The drone successfully measured concentrations of CO, CO2, O3, SO2, and NO2 in affected areas, providing valuable insights for firefighting operations. Different levels of gases have been measured depending on the concentration from burning alcohol, clothes, plastic materials, paper, leaves, and so on. The novelty of this work lies in the development and comprehensive analysis of an IoT-based firefighting drone conducting extensive real-time experiments.

MACHINE LEARNING AND BLOCKCHAIN-BASED REAL-TIME FACIAL RECOGNITION ATTENDANCE SYSTEM

In a vast majority of fields, the use of facial recognition for authentication is expanding. In this information age, authentication has become vital, and the need for faster and more secure methods of user authentication has been on the rise. The introduction of image processing technologies such as OpenCV has increased society’s reliance on face recognition. Using blockchain, information could be stored in blocks throughout the blockchain network. Blockchain is an extremely secure means for storing and protecting data from intruders. It is a highly disruptive technology that has the ability to alter every plane of society. This paper intends to implement opensource computer vision (OpenCV) to construct a facial detection model that will be employed in a blockchain-secured Attendance Monitoring System. It will not only automate the attendance procedure but also give the system unassailable security. This system will take a live video feed from a camera using OpenCV and identify the faces of students and record their attendance along with the entry time. The data will be kept in a distributed way over the blockchain network that will be accessible to everyone, but data cannot be manipulated.

Facial Landmark Detection Using Machine Learning

Facial landmark detection is a pivotal task in the field of computer vision, holding significant implications across various applications, including facial analysis, augmented reality, facial recognition, and emotion detection. In visual communication, the face serves as a primary means of conveying information, and humans possess an innate ability to discern a wealth of information about another person's intentions, identity, and emotions merely by observing their facial expressions. The process of facial landmark detection using machine learning involves training a model on a dataset comprising real-time facial images, where each image is meticulously annotated with the coordinates of crucial facial landmarks. These landmarks typically include points representing the eyes, nose, mouth, and other facial features. Once trained, the model becomes proficient in recognizing and predicting the precise locations of these landmarks in new, unseen images and videos. The primary objective of this work is to predict facial landmarks in real-time using a live webcam input. This task is achieved through a two-step process. First, a Convolutional Neural Network (CNN) is trained on a comprehensive dataset, such as the 300W-LP dataset available on platforms like Kaggle. This dataset consists of facial images meticulously annotated with 68 distinct facial landmarks. The CNN learns to extract intricate facial features and landmark relationships from this training data, enabling it to recognize these landmarks with high accuracy. With the trained CNN in place, the next step is to extend its capabilities to perform facial landmark detection on live video streams. This involves integrating face detection techniques to identify and isolate faces within the live video feed. Once a face is detected, the CNN can then be applied to predict the locations of the 68 facial landmarks in real-time, enhancing our ability to understand and interpret facial expressions and movements in dynamic scenarios. In summary, this work underscores the significance of facial landmark detection in various computer vision applications. By training a CNN on a dataset of annotated facial images and integrating it with live video feeds, we aim to empower machines with the ability to understand and interpret human facial expressions and gestures in real-time, opening up new possibilities for applications in fields like human-computer interaction, emotion analysis, and beyond.

CNN Based Facial Recognition with Age Invariance

Abstract : As the world has seen exponential expansion over the previous decade, there has been an unusual increase in the crime rate as well as an increase in the number of criminals/missing persons. Face recognition can extract the individualistic characteristics of the human face. A straightforward and adaptable biometric technology is face recognition. The technology used to recognize and identify faces in images or videos is called face detection and recognition. The process of removing facial features has gotten easier as technology has advanced. This study describes the use of an automated security camera for realtime face recognition. With this system, we can instantly identify and detect the faces of the criminals in a live video feed captured by a camera. Criminal records typically include the offender's picture and personal information. Thus, we are able to use these photos in addition to his information. The security camera's recorded footage is transformed into frames. After a face is identified in a frame, it undergoes pre-processing and feature extraction. The characteristics of the real-time image processing are compared to those of the images kept in the criminal database.

Feasibility of Live Video Feed Transmission from Unmanned Aerial Vehicles for Medical Surveillance During the 2022 Montreal Marathon.

In recent years, unmanned aerial vehicles (UAVs) have been increasingly used for medical surveillance purposes in mass-gathering events. No studies have investigated the reliability of live video transmission from UAVs for accurate identification of distressed race participants in need of medical attention. The aim of this study was to determine the proportion of time during which live medical surveillance UAV video feed was successfully transmitted and considered of sufficient quality to identify acute illness in runners participating in the 2022 Montreal Marathon (Canada). Four UAVs equipped with high-resolution cameras were deployed at two pre-defined high-risk areas for medical incidents located within the last 500 meters of the race. The video footage was transmitted in real-time during four consecutive hours to a remote viewing station where four research assistants monitored it on large screens. Interruptions in live feed transmission and moments with inadequate field of view (FOV) on runners were documented. On September 25, 2022, a total of 6,916 athletes ran during the Montreal Marathon and Half Marathon. Out of the eight hours of video footage analyzed (four hours per high-risk area), 91.7% represented uninterrupted live video feed with an adequate view of the runners passing through the high-risk areas. There was a total of 18 live feed interruptions leading to a total interruption time of 22 minutes and 19 seconds (median interruption time of 32 seconds) and eight distinct moments with inadequate FOV on runners which accounted for 17 minutes and 33 seconds (median of 1 minute 47 seconds per moments with inadequate FOV). Active surveillance of drone-captured footage allowed early identification of two race participants in need of medical attention. Appropriate resources were dispatched, and UAV repositioning allowed for real-time viewing of the medical response. Live video transmission from UAVs for medical surveillance of runners passing through higher risk segments of a marathon for four consecutive hours is feasible. Live feed interruptions and moments with inadequate FOV could be minimized through practice and additional equipment redundancy.

Smart Surveillance Drone – Warehouse Operations

Drones have recently gained in popularity and are now frequently used for a variety of purposes. The majority of commercially available drones are generally non-self-governing and require the assistance of a human operator. However, advances in computer vision and artificial intelligence in general have drastically altered the situation. The growing scale of warehouses, as well as the difficulty in hiring trained workers, rising demand for customer services, and the rise of e-commerce, have heightened the need for warehouse operations to be more efficient through automation. In warehouses, drones may be a feasible alternative to manual inspections and surveillance activities. They can also be used for intralogistics, such as transferring parts from warehouses to assembly lines in factories. Multiple organizations throughout the world have been doing product delivery studies, but the focus of this article will be on the use of drones in warehouses for inventory management, which is gaining traction every day. Inventory management, stock inspection, and visual feedback will be the project's main applications. Manually inspecting the condition of things or confirming the contents of a product is common. The system makes it possible to do so fast and efficiently. The method allows still photographs to be collected for subsequent processing or a live video feed for FPV viewing because the drones are equipped with cameras. The report contains a thorough examination of the done systems and their application in warehouse management

Survey on Virtual Dressing Room System Using Deep Neural Networks

Abstract: In earlier twenty first century, fashion trends has evolved into a way of life. Depending on skin tone, physical stature, gender, as well as social and geographic factors, the amount and style of clothes which we worn might vary significantly. For the huge chunk of population , the in-store buying experience ( buying in the shops ) is still what comes to mind when they think of shopping. The customers can try on clothing in real time, but this process takes too long when there will not be enough trial rooms in the dressing store. The goal here is to create an engaging, interactive, involving and incredibly realistic virtual system that allow the users or customers to select from a wide variety of clothing designs before simulating those outfits on the people or customers virtually. In this study, we have proposed a system that aids in the synchronization of daily attire of the people. The Virtual styling room using live video feed may alter how a person will shop for and tries on new clothing. Customers or people can try on a wide range of clothing items without a need to actually wear them by making use of the idea of “Virtual Reality”. The benefit of doing things in this way is that it would take less time and effort of the people or customers to physically try the garments on. The project we did also aids in market management, reducing the requirement for customers to try on each and every article of clothing in clothing store. Additionally, retailers can save time, money, and space by not keeping a large inventory on hand( in store).

Virtual Dressing Room System using Deep Neural Networks

Abstract: In earlier twenty first century, fashion trends has evolved into a way of life. Depending on skin tone, physical stature, gender, as well as social and geographic factors, the amount and style of clothes which we worn might vary significantly. For the huge chunk of population, the in-store buying experience ( buying in the shops ) is still what comes to mind when they think of shopping. The customers can try on clothing in real time, but this process takes too long when there will not be enough trial rooms in the dressing store. The goal here is to create an engaging, interactive, involving and incredibly realistic virtual system that allow the users or customers to select from a wide variety of clothing designs before simulating those outfits on the people or customers virtually. In this study, we have proposed a system that aids in the synchronization of daily attire of the people. The Virtual styling room using live video feed may alter how a person will shop for and tries on new clothing. Customers or people can try on a wide range of clothing items without a need to actually wear them by making use of the idea of “Virtual Reality”. The benefit of doing things in this way is that it would take less time and effort of the people or customers to physically try the garments on. The project we did also aids in market management, reducing the requirement for customers to try on each and every article of clothing in clothing store. Additionally, retailers can save time, money, and space by not keeping a large inventory on hand (in store). This project aims at transferring a target clothing image onto a reference person. Generative adversarial network involves three modules semantic generation, cloth wrapping and try on module

Wireless Voice Controlled Robotic Car

The Wireless Voice Control Robotic Car project is a system used for security purposes. Although many wireless-controlled robots use RF modules, this project utilizes an Android smartphone for robotic control, with more control commands available compared to RF modules. To use this project, the user must install an application on their mobile device and turn on Bluetooth. Various commands, such as "go ahead," "get back," "turn left," "turn right," "stop," "break," and "show me your dance" are available. While the Robotic Car is in action, the user can watch a live video feed from the camera installed on it. In order to successfully connect to the live feed, users must configure their system with a username and password. Successful authentication is necessary to ensure that the data remains confidential. Once authentication and configuration are complete, the user has several options for camera settings

Ultrasonic Spectacles for Blind Person

The ultrasonic spatial code line for blind people using Node MCU, web camera, ultrasonic sensor, and output of speaker is an innovative system designed to aid visually impaired individuals in navigating their environment independently and safely. The system works by using a combination of hardware components and software algorithms to provide real-time spatial feedback to the user. The system consists of a Node MCU microcontroller, which acts as the central processing unit and controls the flow of data between the various components. A web camera is used to capture a live video feed of the user's surroundings, which is then processed using image recognition algorithms to identify and locate obstacles in the environment. An ultrasonic sensor is used to measure the distance between the user and any obstacles detected in the video feed. This information is then used to generate a tactile feedback map that is transmitted to the user via a speaker output. The tactile feedback map consists of a series of vibrations that correspond to the location and distance of the obstacles in the user's environment. The ultrasonic spatial code line is a critical need for blind individuals who struggle with navigating their environment independently. The system provides a practical solution that can help improve their quality of life by enabling them to move around more safely and confidently. Additionally, the system is relatively low-cost and easy to implement, making it accessible to a broader range of individuals. Key Word: Electronic Travel Aid – Visual Impairment- Machine learning – Navigation Aid - Mobility – Ultrasonic spectacles – Blind navigation;

Feasibility of Live Video Feed Transmission from UAVs for Medical Surveillance During the 2022 Montreal Marathon

Introduction:In recent years, unmanned aerial vehicles (UAVs) have been increasingly used for medical surveillance purposes in mass gathering events. No studies have investigated the reliability of live video transmission from UAVs for accurate identification of distressed race participants in need of medical attention. During the 2022 Montreal marathon, the aim of this study was to determine the proportion of data collection time during which live medical surveillance UAV video feed was successfully transmitted and considered of sufficient quality to identify acute illness.Method:Four UAVs equipped with high resolution cameras were deployed at two predefined high-risk areas for medical incidents located within the last 800m of the race. The video footage was transmitted in real-time during four consecutive hours to a remote viewing station where four research assistants monitored it on large screens. Interruptions in live feed transmission and moments with inadequate field of view on runners were documented.Results:On September 25, 2022, 8,577 athletes registered in the Montreal marathon and half marathon. Out of the eight hours of video footage analyzed (four hours per high-risk area), 91.7% represented uninterrupted live video feed with an adequate view of the runners passing through the high-risk areas. The total interruption time was 22 minutes and 19 seconds, and the field of view was considered inadequate for a total of 17 minutes and 33 seconds. Active surveillance of drone-captured footage allowed identification of two race participants in need of medical attention. Appropriate resources were dispatched, and UAV repositioning allowed for real-time viewing of the medical response.Conclusion:Live video transmission from UAVs for medical surveillance of runners passing through higher-risk segments of a marathon race for four consecutive hours is feasible. Live feed interruptions and segments with an inadequate field of view could be minimized through practice and additional equipment redundancy.

Dance and the Mediated Immersive Flux in Carlos Saura’s Musical Hybrids with Live Feed

Abstract Carlos Saura’s “pure musicals,” as he calls them, are highly based on the formal properties of the image and the expressive use of light in a minimalist scenic space. Although, they have not been declared screendance pieces (as per Rosenberg 2012), which conjoin rhythmic body movements with screen-based, technologically mediated methods of rendering, they are full-fledged screendance examples, being hybrid, symbiotic, and integral (Richard James Allen, 2006). This article concentrates on Saura’s musicals from 2005 onwards – Flamenco (1995), Iberia (2005), Fados (2007), Flamenco Flamenco (2010), Argentina (Zonda, folclore argentino, 2015) and Jota de Saura (2016) – particularly the immersive mediation operated through the use of live video feed as an intermedial sensorial device. Saura’s silky, glossy, and lustrous images form an optical-haptic continuum. The twofold bodies, the digital doubles and the flesh-and-bone act as inducers of crystallization in Gilles Deleuze’s perception of modern cinema (1985), inasmuch as they interact and alternate in a cinematic flux, forming a circuit. Thus, an image of a recorded stage performance enters into a relationship with cinema, a medium already endowed with reflective features, producing the crystallization of these screendance films in all their Saurian immersivenness and sensoriality.

Searching in the sand: Protracted video deficit in U.S. preschoolers' spatial recall using a continuous search space.

Young children exhibit a video deficit for spatial recall, learning less from on-screen than in-person demonstrations. Some theoretical accounts emphasize memory constraints (e.g., insufficient retrieval cues, competition between memory representations). Such accounts imply memory representations are graded, yet video deficit studies measuring spatial recall operationalize memory retrieval as dichotomous (success or failure). The current study tested a graded-representation account using a spatial recall task with a continuous search space (i.e., sandbox) rather than discrete locations. With this more sensitive task, a protracted video deficit for spatial recall was found in children 4-5years old (n=51). This may be due to weaker memory representations in the screen condition, evidenced by higher variability and greater perseverative bias. In general, perseverative bias decreased with repeated trials. The discussion considers how the results support a graded-representation account, potentially explaining why children might exhibit a video deficit in some tasks but not others. RESEARCH HIGHLIGHTS: The task used a continuous search space (sandbox), making it more difficult and sensitive than spatial recall tasks used in prior video deficit research. Spatial recall among 4- and 5-year-old children was more variable after watching hiding events on screen via live video feed than through a window. Children's spatial recall from screens was more susceptible to proactive interference, evidenced by more perseverative bias in an A-not-B design. The results demonstrate memory representations blend experiences that accumulate over time and explain why the video deficit may be protracted for more difficult tasks.