Webcam Research Articles

The Use of Imaging to Quantify the Impact of Seed Aging on Lettuce Seed Germination and Seedling Vigor.

The decline in seed quality over time due to natural aging or mishandling requires assessing seed vigor for resilience in adverse conditions. Accelerated aging (AA) methods simulate seed deterioration by subjecting seeds to high temperatures and humidity. Saturated salt accelerated aging (SSAA) is an AA method adopted for small seeds like lettuce (Lactuca sativa). In this study, we subjected seeds of two lettuce cultivars ('Muir' and 'Bauer') to SSAA by sealing them in a box containing 40 g/100 mL of a sodium chloride (NaCl) solution in a dark growth chamber at 41 °C for 24, 48, and 72 h with a control. We monitored their vigor using embedded computer cameras, tracking the projected canopy size (PCS) daily from sowing to harvest. The cultivar 'Muir' exhibited consistent PCS values across the treatments, while 'Bauer' showed PCS variations, with notable declines after prolonged aging. The germination rates dropped significantly after 48 and 72 h of SSAA. A nonlinear regression model revealed a strong relationship between PCS and shoot dry weight across harvests and cultivars (R2 = 0.93, RMSE = 0.15, p < 0.001). The research found that the projected canopy size and shoot dry weight increased over time with significant differences in treatments for the cultivar 'Bauer' but not for 'Muir,' with the canopy size being a strong predictor of dry weight and no significant impact from the SSAA treatments. This study highlights cultivar-specific responses to aging and demonstrates the efficacy of our imaging tool in predicting lettuce dry weight despite treatment variations. Understanding how aging affects different lettuce varieties is crucial for seed management and crop sustainability.

Simulation-driven design of smart gloves for gesture recognition

Smart gloves are in high demand for entertainment, manufacturing, and rehabilitation. However, designing smart gloves has been complex and costly due to trial and error. We propose an open simulation platform for designing smart gloves, including optimal sensor placement and deep learning models for gesture recognition, with reduced costs and manual effort. Our pipeline starts with 3D hand pose extraction from videos and extends to the refinement and conversion of the poses into hand joint angles based on inverse kinematics, the sensor placement optimization based on hand joint analysis, and the training of deep learning models using simulated sensor data. In comparison to the existing platforms that always require precise motion data as input, our platform takes monocular videos, which can be captured with widely available smartphones or web cameras, as input and integrates novel approaches to minimize the impact of the errors induced by imprecise motion extraction from videos. Moreover, our platform enables more efficient sensor placement selection. We demonstrate how the pipeline works and how it delivers a sensible design for smart gloves in a real-life case study. We also evaluate the performance of each building block and its impact on the reliability of the generated design.

Breaking the silence: empowering the mute-deaf community through automatic sign language decoding.

The objective of this study is to develop a system for automatic sign language recognition to improve the quality of life for the mute-deaf community in Egypt. The system aims to bridge the communication gap by identifying and converting right-hand gestures into audible sounds or displayed text. To achieve the objectives, a convolutional neural network (CNN) model is employed. The model is trained to recognize right-hand gestures captured by an affordable web camera. A dataset was created with the help of six volunteers for training, testing, and validation purposes. The proposed system achieved an impressive average accuracy of 99.65 % in recognizing right-hand gestures, with high precision value of 95.11 %. The system effectively addressed the issue of gesture similarity between certain alphabets by successfully distinguishing between their respective gestures. The proposed system offers a promising solution for automatic sign language recognition, benefiting the mute-deaf community in Egypt. By accurately identifying and converting right-hand gestures, the system facilitates communication and interaction with the wider world. This technology has the potential to greatly enhance the quality oflife for individuals who are unable to speak or hear, promoting inclusivity and accessibility.

SunbYte: an autonomous pointing framework for low-cost robotic solar telescopes on high altitude balloons

The design and usability of a fully autonomous robotic control system (SunbYte - Sheffield University Balloon “lYfted” TElescope) for solar tracking and observational applications onboard high-altitude balloons are addressed here. The design is based on a six-step development plan balancing scientific objectives and practical engineering requirements. The high-altitude solar observational system includes low-cost components such as a Cassegrain-type telescope, stepper motors, harmonic drives, USB cameras and microprocessors. OpenCV installed from ROS (Robotic Operating System), python and C facilitated the collection, compression, and processing of housekeeping and scientific data. This processed data was then transmitted to the ground station through the launch vehicle’s telecommunication link. The SunbYte system allows the brightest spot in the sky, the sun, to be identified, and a telescope pointed towards it with high enough accuracy that a scientific camera can capture images. This paper gathers and presents the results from primarily two missions with the High-Altitude Student Platform (HASP, NASA Balloon Program office and LaSpace). Additionally, a discussion will be made comparing these with an earlier iteration flown with the German-Swedish “REXUS/BEXUS” programme coordinated by the European Space Agency. By capturing and analysing a series of tracking images with the location of the Sun at the calibrated centre, the system demonstrated the tracking capabilities on an unstable balloon during ascent. Housekeeping sensor data was collected to further analyse the thermal and mechanical performance. The low temperature increased friction in the drive train and reduced the responsiveness of the harmonic drive actuation system. This caused some issues which require further work in future missions, for example, with SunbYte 4 and its work when flying with the HEMERA ZPB (Zero Pressure Balloon) program.

Relationship Between Frequency of Remote Visitation and Postpartum Depression in Mothers of Hospitalized Neonates During the COVID-19 Pandemic

ObjectiveTo explore the relationship between symptoms of postpartum depression and the number of remote visitations among mothers of infants in the NICU. DesignRetrospective cohort study. SettingNICU in a medical university in Iwate, Japan. ParticipantsA total of 89 mothers of infants who spent more than 1 month in the NICU from June 2021 to December 2022. MethodsParticipants completed the Edinburgh Postnatal Depression Scale (EPDS) at 4 days and 1 month after birth. We used a one-way analysis of variance with Tukey–Kramer or Games–Howell post hoc tests to examine differences in postpartum depression among three groups based on the frequency of remote visitation: frequent visitation, rare visitation, or no visitation. ResultsOf the 89 mothers, 41 scored 9 points or higher on the EPDS conducted 4 days after birth; among them, 14 did not visit, 13 rarely visited, and 14 frequently visited the NICU remotely through a web camera. The rare visitation group had significantly higher EPDS scores 1 month after birth (M = 9.7, SD = 5.2) than the frequent (M = 5.3, SD = 3.7) and no visitation (M = 5.1, SD = 4.2) groups (p < .05). The rare visitation group demonstrated lower improvement on the EPDS than the frequent and no visitation groups (nonsignificant). ConclusionIt is unclear whether remote visitation reduces symptoms of postpartum depression; however, the frequency of remote visitation could be assessed to identify at-risk mothers in need of social support.

Design of Hybrid Learning in Classroom Using Web Camera and Website

The Covid pandemic that hit the world including Indonesia caused changes in lifestyle and ways of working and studying to prevent the transmission of COVID-19 virus. This is certainly a challenge for education in the era of the pandemic on how the learning process can be proceeded so the knowledge is conveyed. Hybrid learning is a combination of some learning methods; online method (outside classroom) with face-to-face meeting method for several hours (inside classroom). Therefore, hybrid learning is suitable to be carried out during this pandemic. Hybrid learning is built in a class that is built based on Internet and uses web camera technology that can be done anywhere with the Internet by students and lecturers. This class provides hybrid learning process using website and 4G internet network so that students get a better learning experience and understanding easier to learn materials. The web camera is utilized as a student online learning process that views the movement of the lecturer so that it makes learning easier for the student. This hybrid learning also uses website to support in-class monitoring and future hybrid learning processes and capable of recording the learning process so the recording can be used in the future.

Drowsiness Detection in the Advanced Driver-Assistance System using YOLO V5 Detection Model

The development of Artificial Intelligence (AI) in the automobile industry, particularly in the Advanced Driver-Assistance System (ADAS), has been increasing rapidly in recent years. One of the essential features of ADAS is the drowsiness alert system, which monitors the state of the driver's eyes. This article presents a study on the development of ADAS that focuses on drowsiness detection using deep learning through a Convolutional Neural Network (CNN) approach. The study utilized the YOLO V5 model for object detection, which was trained using custom datasets containing images annotated with two labels: "Mengantuk" (Drowsy) and "Bangun" (Awake) The goal was to recognize whether the driver was drowsy or awake. The results of the study showed that the modified YOLO V5 CNN Model had high accuracy based on evaluation metrics in terms of accuracy, precision, and recall in detecting drowsiness around the area of the eyes, with a Mean Average Precision (mAP) of 99.5% and an F1-Score of 99.8%. For better understanding and visualization, the model was tested using real-time detection through a web camera, using Jetson Nano as the inference device. The model detected drowsiness in real time, with a confidence rate of 80% to 97%

Face Recognition Attendance System

Attendance taking is one of a tedious and important tasks in colleges, universities, organizations, schools, and offices, which must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The main goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, sec, and photographs, is trained. The images are extracted using Open CV. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. Logitech C270 web camera and NVIDIA Jetson Nano Developer kit were used in this project as the camera and processing board. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.

Enhanced real-time road-vehicles’ detection and tracking for driving assistance

Road-object detection, recognition, and tracking are vital tasks that must be performed reliably and accurately by self-driving car systems in order to achieve the automation/autonomy goal. Other vehicles are one of the main objects that the egocar must accurately detect and track on the road. However, deep-learning approaches proved their effectiveness at the expense of very demanding computational power and low throughput. They must be deployed on expensive CPUs and GPUs. Thus, in this work, a lightweight vehicle detection and tracking technique (LWVDT) is suggested to fit low-cost CPUs without sacrificing robustness, speed, or comprehension. The LWVDT is suitable for deployment in both advanced driving assistance systems (ADAS) functions and autonomous-car subsystems. The implementation is a sequence of computer-vision techniques fused together and merged with machine-learning procedures to strengthen each other and streamline execution. The algorithm details and their execution are revealed in detail. The LWVDT processes raw RGB camera pictures to generate vehicle boundary boxes and tracks them from frame to frame. The performance of the proposed pipeline is assessed using real road camera images and video recordings under different circumstances and lighting/shading conditions. Moreover, it is also tested against the well-known KITTI database, achieving an average accuracy of 87%.

Human Emotion Detection Using CNN and Transfer Learning

Facial emotion detection is a critical component in human-computer interaction, mental health assessment, and security systems. In this project, we propose a robust facial emotion detection system leveraging state-of-the-art deep learning techniques. Our system utilizes Convolutional Neural Networks (CNNs) to extract meaningful features from facial images and classify them into five distinct emotional categories: neutral, surprise, sad, happy, and angry. We conducted extensive experiments on a diverse dataset consisting of over 10,000 annotated facial images collected from various sources. Through data augmentation techniques such as rotation, translation, and flipping, we expanded the dataset to enhance model training. Additionally, we employed transfer learning by fine-tuning a pre-trained CNN model, ResNet50, on our dataset to leverage its learned features. This project presents a system for real-time emotion monitoring using computer vision techniques. The system utilizes the Haar Cascade Classifier for face detection in live webcam video streams. Furthermore, we evaluated the system's performance across different lighting conditions, poses, and facial occlusions to assess its robustness in real-world scenarios. Our results indicate that the system maintains consistent performance across diverse conditions, making it suitable for deployment in applications requiring real-time facial emotion recognition.

IOT- Based Human Friendly Smart Trolley

Shopping and buying is an integral part of our daily lives. Big mega markets have a wide variety of items and different stores can have different deliveries of goods. It is difficult for many consumers to stand in the long queue for the billing of goods purchased. This causes wastage of money and a poor bill for the wrong customer. Trolleys are used in supermarkets or grocery stores to make shopping simpler. However, it is difficult for customers to control the trolley while shopping. An automated customer following trolley was implemented which calculates the total sum of grocery items carried in the trolley by the customers. This reduces the customer's effort to pull the trolley and keep the line for the payment of the grocery products. The customer who has a specific tag and a web camera installed in front of the trolley will recognize the tag and move the trolley to the customer. Using the RFID tag and the Raspberry Pi receiver, the item bill inserted has been obtained in the trolley. This results in an ideal solution to all these problems.

Assessing two methods of webcam-based eye-tracking for child language research.

We assess the feasibility of conducting web-based eye-tracking experiments with children using two methods of webcam-based eye-tracking: automatic gaze estimation with the WebGazer.js algorithm and hand annotation of gaze direction from recorded webcam videos. Experiment 1 directly compares the two methods in a visual-world language task with five to six year-old children. Experiment 2 more precisely investigates WebGazer.js' spatiotemporal resolution with four to twelve year-old children in a visual-fixation task. We find that it is possible to conduct web-based eye-tracking experiments with children in both supervised (Experiment 1) and unsupervised (Experiment 2) settings - however, the webcam eye-tracking methods differ in their sensitivity and accuracy. Webcam video annotation is well-suited to detecting fine-grained looking effects relevant to child language researchers. In contrast, WebGazer.js gaze estimates appear noisier and less temporally precise. We discuss the advantages and disadvantages of each method and provide recommendations for researchers conducting child eye-tracking studies online.

MOUSE AND KEYBOARD MOTION SENSING USING HAND SIGNS

The project presents a way for Human- Computer Interaction (HCI) that employs a web camera or an integrated camera to control the movement of the indicator instead to the current methods, which typically involve manually entering buttons or altering the positions of a physical mouse. Instead, it can perform the functions that a physical computer mouse does, and it does it through camera and technology for computer vision to manage different mouse events. The Virtual Mouse color recognition application constantly acquires real-time image data and processes it using several filtering and conversion phases. The application utilizes an image processing technique to obtain the coordinates of the desired colors that have emerged within the frames holding a list of color combinations, each of them correlates to a different mouse function. The program will run the mouse function and transform it to an actual mouse function which will be able to applied on the user’s PC if the current color combination passes. The main aim of the project is to virtually interface with the system in virtually every manner imaginable. Keywords: Gesture Recognition, Human Computer Interactions, Graphical User Interface, Open-Source Computer Vision.

IOT BASED COLOR AND SIZE WISE SORTING MACHINE USING CAMERA

The process of arranging items systematically is named sorting. Manual sorting of Object is preferred at the wholesale market and industries supported different parameters like size, shape, quality, etc. But it's a time consuming, less efficient, and inconsistent method. The existing systems in the market can sort single Object with single or multiple parameters. To replace this traditional sorting way, the proposed system presents an automatic Object sorting mechanism with an image processing technique. It recognizes and classifies two different Object with two different Color analysis method. Key Words: Image Processing, Python, Arduino Uno, Color Detection, IR Sensor Web Camera, Conveyor Belt.

Perceived Barriers of Managing Teaching–Learning during COVID-19 Pandemic: A Descriptive Observational Study among Medical Educators in West Bengal

Introduction: The COVID-19 pandemic led to a paradigm shift in the landscape of medical education, necessitating a transition from traditional pedagogical methods to digital learning platforms. This study aims to find out the barriers faced by medical educators in West Bengal during the pandemic. Materials and Methods: This descriptive type of observational study with a cross-sectional design was conducted between December 2022 and February 2023 with a reference period of March 2020 to December 2020. The study population was the medical educators working during the reference period in the various departments of Government Medical Colleges in West Bengal. One hundred and twenty medical educators were selected using simple random sampling and responses were collected through a predesigned and pretested Google Forms. Collected data were checked for consistency and analyzed in GNU PSPP (v 1.4.2). Quantitative data were expressed as mean (±standard deviation) or median (±interquartile range), and qualitative data were expressed in frequency and percentage. Results: Out of the 120 sample population, 100 responded within the stipulated 7 days with a response rate of 83%. Most of the respondents had opined that they did not have a dedicated e-classroom facility for online education (87, 87%) and web camera (63, 63%). The majority had a prevailing teacher-centric approach to education including a lack of incorporating student feedback in deciding mode of communication. Conclusion: The study identifies the deficiency of logistical support and identifies the importance of capacity building of medical educators for tiding over similar situations in future.

Reliable Out-of-Distribution Recognition of Synthetic Images.

Generative adversarial networks (GANs) and diffusion models (DMs) have revolutionized the creation of synthetically generated but realistic-looking images. Distinguishing such generated images from real camera captures is one of the key tasks in current multimedia forensics research. One particular challenge is the generalization to unseen generators or post-processing. This can be viewed as an issue of handling out-of-distribution inputs. Forensic detectors can be hardened by the extensive augmentation of the training data or specifically tailored networks. Nevertheless, such precautions only manage but do not remove the risk of prediction failures on inputs that look reasonable to an analyst but in fact are out of the training distribution of the network. With this work, we aim to close this gap with a Bayesian Neural Network (BNN) that provides an additional uncertainty measure to warn an analyst of difficult decisions. More specifically, the BNN learns the task at hand and also detects potential confusion between post-processing and image generator artifacts. Our experiments show that the BNN achieves on-par performance with the state-of-the-art detectors while producing more reliable predictions on out-of-distribution examples.

ДОСЛІДЖЕННЯ ЧИСЛОВОЇ ВИПАДКОВОЇ ПОСЛІДОВНОСТІ, ЩО ОДЕРЖАНА З ВЕБ КАМЕРИ

The investigation object of this paper is a random numbers sequence (RNS) obtained from a single frame of a webcam, which can be practically used as a source of chaos for a hardware RNS generator. The problem under consideration was to calculate the primary statistical characteristics of the RNS obtained from the webcam frame and compare them with the crypto-resistance requirements. The obtained statistics confirmed the hypothesis of a high level of chaos using a webcam. In successive frames from image capture (40 milliseconds gap), the photodiode matrix records changes that are not visible to the human eye. Even in absolute darkness , 60% of the changes in the brightness values of the matrix pixels are recorded. Which is 10% higher than the software engineering crypto-resistance requirements for the avalanche effect parameter. This is probably explained by the chaos that accompanies the stochastic interaction of photons of light with the atoms of the material of the sensor. Thus, it is possible to speak about a high level of chaos in the generated RNS. The peculiarity of the research is that for the purity of the experiment, frame generation was carried out in total darkness (approx. luminosity 10-4 lux), and a uniformly illuminated (luminosity 200 lux) white surface. Testing the web camera under extreme conditions gives a complete picture of the unpredictability and chaos of RNS generation. The primary investigation of the statistics of the generated RNS showed a good compliance with some crypto-resistance requirements. The method built on this property allows designing an affordable hardware RNS generator in laboratory conditions without the involvement of special equipment.

ACCESSIBILITY SYSTEM TO CONTROL LAPTOP WITHOUT MOUSE OR KEYBOARD

The pandemic has brought about a significant change in technology, leading to the creation of a virtual mouse as a substitute for a traditional one. This innovative system utilizes a webcam or built-in camera to capture frames, which are then analyzed through computer vision to enable tracking. Users can control mouse functions by performing gestures, making it a device-free solution, although it does require a battery and a connected dongle. The development and adoption of this virtual mouse represents an active area of research. In basic terms, the system captures images with a camera, processes them for tracking, and responds to user gestures for mouse movement, ultimately eliminating the need for a physical device. Key Words: Virtual Mouse, real camera, web camera, media pipe, computer vision, images capture or video.

Operation and Productivity Monitoring from Sound Signal of Legacy Pipe Bending Machine via Convolutional Neural Network (CNN)

This study introduces a non-invasive approach to monitor operation and productivity of a legacy pipe bending machine in real-time based on a lightweight convolutional neural network (CNN) model and internal sound as input data. Various sensors were deployed to determine the optimal sensor type and placement, and labels for training and testing the CNN model were generated through the meticulous collection of sound data in conjunction with webcam videos. The CNN model, which was optimized through hyperparameter tuning via grid search and utilized feature extraction using Log-Mel spectrogram, demonstrated notable prediction accuracies in the test. However, when applied in a real-world manufacturing scenario, the model encountered a significant number of errors in predicting productivity. To navigate through this challenge and enhance the predictive accuracy of the system, a buffer algorithm using the inferences of CNN models was proposed. This algorithm employs a queuing method for continuous sound monitoring securing robust predictions, refines the interpretation of the CNN model inferences, and enhances prediction outcomes in actual implementation where accuracy of monitoring productivity information is crucial. The proposed lightweight CNN model alongside the buffer algorithm was successfully deployed on an edge computer, enabling real-time remote monitoring.

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

The significant increase in online activities in the wake of recent global events has underlined the importance of biometric person authentication on digital platforms. Although many biometric devices may be used for precise biometric authentication, acquiring the necessary technology, such as 3D sensors or fingerprint scanners, can be prohibitively expensive and logistically challenging. Addressing the demands of online environments, where access to specialized hardware is limited, this paper introduces an innovative approach. In this work, by fusing static and dynamic signature data with facial data captured through regular computer cameras, a dataset of 1750 samples from 25 individuals is constructed. Deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional networks (TCN), are employed to craft a robust multi-classification model. This integration of various deep learning algorithms has demonstrated remarkable performance enhancements in biometric authentication. This research also underscores the potential of merging dynamic and static biometric features, derived from readily available sources, to yield a high-performance recognition framework. As online interactions continue to expand, the combination of various biometric modalities holds potential for enhancing the security and usability of virtual environments.