Camera-based Method Research Articles

Full-Body Pose Estimation of Humanoid Robots Using Head-Worn Cameras for Digital Human-Augmented Robotic Telepresence

We envision a telepresence system that enhances remote work by facilitating both physical and immersive visual interactions between individuals. However, during robot teleoperation, communication often lacks realism, as users see the robot’s body rather than the remote individual. To address this, we propose a method for overlaying a digital human model onto a humanoid robot using XR visualization, enabling an immersive 3D telepresence experience. Our approach employs a learning-based method to estimate the 2D poses of the humanoid robot from head-worn stereo views, leveraging a newly collected dataset of full-body poses for humanoid robots. The stereo 2D poses and sparse inertial measurements from the remote operator are optimized to compute 3D poses over time. The digital human is localized from the perspective of a continuously moving observer, utilizing the estimated 3D pose of the humanoid robot. Our moving camera-based pose estimation method does not rely on any markers or external knowledge of the robot’s status, effectively overcoming challenges such as marker occlusion, calibration issues, and dependencies on headset tracking errors. We demonstrate the system in a remote physical training scenario, achieving real-time performance at 40 fps, which enables simultaneous immersive and physical interactions. Experimental results show that our learning-based 3D pose estimation method, which operates without prior knowledge of the robot, significantly outperforms alternative approaches requiring the robot’s global pose, particularly during rapid headset movements, achieving markerless digital human augmentation from head-worn views.

Boiling heat transfer of upwind planar water jet impingement on moving hot steel sheet

This study aimed to experimentally investigate the boiling heat transfer characteristics of an upward planar water jet on a horizontally moving stainless-steel sheet with 0.3-mm thickness using an infrared camera-based method. The experiments varied the moving velocity of the solid (1.5, 3.0, and 4.4 m/s), the initial temperature (200–600 °C), and the jet impact velocity (1.08 and 1.32 m/s). The surface heat flux distribution and temperature profile of the solid were found to be asymmetric to the jet impingement line. The temperature drop due to jet impingement ΔTm can be expressed as ∫qs(x)dx/(ρcHVs), where ρ,c,H are the density, specific heat, and thickness of the test sheet, respectively. This quantity depends both the heat flux profile qs(x) and the moving velocity Vs. The solid exhibited large temperature decreases at small moving velocities. The curve of the maximum heat flux, defined as the peak value in each experiment, exhibited similarities to a pool boiling curve and was significantly dependent on the local temperature of the solid. The empirical correlations for predicting the maximum heat flux in the nucleate and film boiling regions were constructed as qmax=50100VS-0.169vj0.377ΔTsat1.48 W/m2 and qmax=34500VS0.014vj0.5ΔTsat W/m2, respectively, where vj, and ΔTsat are the jet impact velocity and wall superheat.

Online Monitoring of Iron Ore Pellet Size Distribution Using Lightweight Convolutional Neural Network

An improved U-Net based convolutional neural network (CNN) is proposed in this paper for online size monitoring of iron ore pellets. The proposed CNN model is a lightweight version of U-Net. In the proposed model each convolution block of the encoder subnet consists of a concatenated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 1$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$</tex-math> </inline-formula> filter kernel, a batch normalization layer, and a ReLU nonlinearity. The proposed CNN model efficiently detects the pellets even under variable illumination conditions and images with overlapping pellets. The proposed model is evaluated using images of iron ore pellets acquired at the discharge end of a pelletizer disc. The proposed CNN model performs satisfactorily using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 39% less learnable parameters, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 16% reduced computational cost than the original U-Net model. The proposed model outperforms the conventional U-Net, different variants of U-Nets, FCN, and SegNet in weighted IoU, BF Score, global accuracy, SSIM, and PSNR. Experimental validation also shows a good agreement between the obtained pellet size distribution and the manual sieving result. This makes the proposed algorithm suitable for online pellet size-distribution measuring instruments. This method provides a more accurate and fast size monitoring system for iron ore pellets, ideal for real-time industrial applications. The image dataset and source codes are available at the below given link. https://github.com/aryadeo/Pellet_Size_Distribution. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Due to the advent of digitalization in industries, several attempts are being made to use innovative industrial monitoring systems in steel manufacturing plants. Most of the pellet plants in steel industries use human supervision for pellet growth monitoring. In this paper, a camera-based pellet size distribution monitoring method is proposed. A novel deep learning-based algorithm is used for the detection of pellets from the captured image followed by computation of pellet size analysis. Due to low computational complexity and better accuracy, the proposed method is suitable for online pellet size distribution system development compared to other previously proposed methods. However, protection from the harsh industrial environment is required for the uninterrupted and continuous operation of the system. As a future scope, the obtained pellet size information can be used as one of the control variables in designing a control system for the automated operation of the pelletizer disc.

Infrastructure 2D Camera-based Real-time Vehicle-centered Estimation Method for Cooperative Driving Support

Infrastructure 2D Camera-based Real-time Vehicle-centered Estimation Method for Cooperative Driving Support

Zonal demand-controlled ventilation strategy to minimize infection probability and energy consumption: A coordinated control based on occupant detection

Due to the outbreak of COVID-19, an increased risk of airborne transmission has been experienced in buildings, particularly in confined public places. The need for ventilation as a means of infection prevention has become more pronounced given that some basic precautions (like wearing masks) are no longer mandatory. However, ventilating the space as a whole (e.g., using a unified ventilation rate) may lead to situations where there is either insufficient or excessive ventilation in localized areas, potentially resulting in localized virus accumulation or large energy consumption. It is of urgent need to investigate real-time control of ventilation systems based on local demands of the occupants to strike a balance between infection risk and energy saving. In this work, a zonal demand-controlled ventilation (ZDCV) strategy was proposed to optimize the ventilation rates in sub-zones. A camera-based occupant detection method was developed to detect occupants (with eight possible locations in sub-zones denoted as ‘A’ to ‘H’). Linear ventilation model (LVM), dimension reduction, and artificial neural network (ANN) were integrated for rapid prediction of pollutant concentrations in sub-zones with the identified occupants and ventilation rates as inputs. Coordinated ventilation effects between sub-zones were optimized to improve infection prevention and energy savings. Results showed that rapid prediction models achieved an average prediction error of 6 ppm for CO2 concentration fields compared with the simulation under different occupant scenarios (i.e., occupant locations at ABH, ABCFH, and ABCDEFH). ZDCV largely reduced the infection risk to 2.8% while improved energy-saving efficiency by 34% compared with the system using constant ventilation rate. This work can contribute to the development of building environmental control systems in terms of pollutant removal, infection prevention, and energy sustainability.

A Digital Camera-Based Eye Movement Assessment Method for NeuroEye Examination

The ability to perform quantitative and automated neurological assessment could enhance diagnosis and treatment in the pre-hospital setting, such as during telemedicine or emergency medical services (EMS) encounters. Such a tool could be developed by adapting clinically significant information such as symmetry of eye movement or conjugate eye movement. Here we describe a digital camera-based eye tracking method “NeuroGaze” to capture the symmetry of eye movement while performing neurological eye examination. The proposed method was developed based on detecting the center of the pupil for both eyes from a given video and measuring eye conjugacy by transforming the pupil center coordinates to relative gaze. The method was tested on healthy volunteers while performing three neurological eye examinations <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> The NeuroEye dataset is made available at <uri>https://www.kaggle.com/datasets/mahassan8/neuroeye</uri> </fn> . We also compared our proposed approach to state-of-the-art digital camera-based eye-tracking methods and commercial off-the-shelf (COTS) eye trackers. NeuroGaze outperformed digital camera-based eye tracking methods by reporting a mean Spearman rank-order correlation coefficient of 0.86 for the H-test, 0.87 for the Dot-test, and 0.56 for the OKN-Test, and shows similarity in trends for the relative gaze trajectories with a noticeable offset in the scale of the relative gaze angle compared to COTS eye tracker (see Fig. 1). The study demonstrates that by using a pupil-center-based eye-tracking method, a digital camera can measure clinically relevant information regarding eye movement.

Concurrent Validity of Motion Parameters Measured With an RGB-D Camera-Based Markerless 3D Motion Tracking Method in Children and Young Adults.

Low-cost, portable RGB-D cameras with integrated motion tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications, particularly when applied to children. In previous work, we developed an RGB-D camera-based motion tracking method and showed that it accurately captures body joint positions of children and young adults in 3D. In this study, the validity and accuracy of clinically relevant motion parameters that were computed from kinematics of our motion tracking method are evaluated in children and young adults. Twenty-three typically developing children and healthy young adults (5-29 years, 110-189 cm) performed five movement tasks while being recorded simultaneously with a marker-based Vicon system and an Azure Kinect RGB-D camera. Motion parameters were computed from the extracted kinematics of both methods: time series measurements, i.e., measurements over time, peak measurements, i.e., measurements at a single time instant, and movement smoothness. The agreement of these parameter values was evaluated using Pearson's correlation coefficients r for time series data, and mean absolute error (MAE) and Bland-Altman plots with limits of agreement for peak measurements and smoothness. Time series measurements showed strong to excellent correlations (r-values between 0.8 and 1.0), MAE for angles ranged from 1.5 to 5 degrees and for smoothness parameters (SPARC) from 0.02-0.09, while MAE for distance-related parameters ranged from 9 to 15 mm. Extracted motion parameters are valid and accurate for various movement tasks in children and young adults, demonstrating the suitability of our tracking method for clinical motion analysis. The low-cost portable hardware in combination with our tracking method enables motion analysis outside of specialized facilities while providing measurements that are close to those of the clinical gold-standard.

The Current State of Nuclear Nephrology in Modern Medicine.

Glomerular filtration rate (GFR) is the most reliable parameter of renal function. Regarding the complexity of the gold standard inulin clearance, different estimating equations have been developed with CKD-EPI creatinine equation recommended as the most reliable one. In some clinical situations where creatinine based equations might not be valid, alternative methods are needed. Nuclear medicine methods for measuring GFR with 51Cr EDTA and 99mTc DTPA have been widely used for decades. There are different methodologies for the measurement of kidney function with radiopharmaceuticals: urinary clearance, plasma clearance, multiple plasma sampling, slope intercept, single sample plasma equation, slope only, and the gamma camera-based method. Greater precision of measuring GFR is needed in certain clinical situations. The most common are diagnosis and follow up of chronic kidney disease and definition of the beginning of replacement therapy. The assessment of renal function is also important for potential kidney donors. In recent years, with the introduction of new chemotherapeutic drugs and targeted therapy, oncologic patients treated with nephrotoxic drugs have become more commonly referred for measuring GFR. The monitoring of renal function is important during treatment in order to detect the transformation from reversible acute kidney injury to irreversible chronic kidney disease as well as in the cases of renal insufficiency reduce the dosage and prevent accumulation of the drug and avoid dosage related toxic effects. Assessment of kidney function using measured mGFR will be an important milestone in the creation of more accurate and expanding personalized medicine principle in current onconephrology practice.

Occupants' interaction with building services: Development of a camera-based method for detailed monitoring of windows, shadings, and lights

Several energy gains or losses in buildings are influenced by occupant interactions with building services, such as lighting or HVAC systems, thermostat settings, and window and shading operations. Occupant behavior is usually triggered by discomfort, nevertheless actions taken to restore comfort can have an impact on final energy demand. Thus, an accurate energy assessment for both new and retrofit building design must properly account for occupant behavior, based on reliable models developed from real case studies and detailed monitoring. This work presents a new approach for continuous and non-intrusive monitoring of window opening angle, shading position, and lighting operation to determine the net air exchange area for ventilation. A camera-based device and a post-processing algorithm are developed, and a monitoring campaign over 6 month is carried out to showcase the monitoring system. The device consists of a camera setup connected to a microprocessor, and a dedicated script which enables the device to track window opening, shading movement and lighting operation through target and object identification. Results of the prototyping case study show that the proposed system can effectively detect window opening angles and shutter positions, dealing with multiple windows and shutters simultaneously and allowing the deployment of the benefits of continuous monitoring. The explored application is the direct use of the collected data for the calculation of natural ventilation rates from the net exchange area (EN 16798-7) over long term datasets. As future development, the monitoring system will be used to develop accurate behavioral models based on the experimental data to analyze and suggest the occupant’s response to discomfortable conditions in order to improve indoor air quality and save energy.

A Proposal for Lodging Judgment of Rice Based on Binocular Camera

Rice lodging is a crucial problem in rice production. Lodging during growing and harvesting periods can decrease rice yields. Practical lodging judgment for rice can provide effective reference information for yield prediction and harvesting. This article proposes a binocular camera-based lodging judgment method for rice in real-time. As a first step, the binocular camera and Inertial Measurement Unit (IMU) were calibrated. Secondly, Census and Grayscale Level cost features are constructed for stereo matching of left and right images. The Cross-Matching Cost Aggregation method is improved to compute the aggregation space in the LAB color space. Then, the Winner-Takes-All algorithm is applied to determine the optimal disparity for each pixel. A disparity map is constructed, and Multi-Step Disparity Refinement is applied to the disparity map to generate the final one. Finally, coordinate transformation obtains 3D world coordinates corresponding to pixels. IMU calculates the real-time pose of the binocular camera. A pose transformation is applied to the 3D world coordinates of the rice to obtain its 3D world coordinates in the horizontal state of the camera (pitch and roll angles are equal to 0). Based on the distance between the rice and the camera level, thresholding was used to determine whether the region to be detected belonged to lodging rice. The disparity map effect of the proposed matching algorithm was tested on the Middlebury Benchmark v3 dataset. The results show that the proposed algorithm is superior to the widely used Semi-Global Block Matching (SGBM) stereo-matching algorithm. Field images of rice were analyzed for lodging judgments. After the threshold judgment, the lodging region results were accurate and could be used to judge rice lodging. By combining the algorithms with binocular cameras, the research results can provide practical technical support for yield estimation and intelligent control of rice harvesters.

Estimating perceived indoor air quality and environmental satisfaction using a camera

Although indoor environmental monitoring data are increasingly available, they often fail to accurately capture personal environmental perception, comfort, and satisfaction. This disconnection has led to a growing interest in using real-time feedback from building users to improve the control of heating, ventilation, and air-conditioning systems and reduce energy waste. However, traditional questionnaire-based surveys only provide a snapshot at the time of investigation and suffer from a variety of bias sources. To address this, this paper proposes a novel camera-based method for estimating perceived indoor air quality and environmental satisfaction by analysing facial expressions. We conducted indoor air pollution exposure experiments on 24 healthy adults in twin environmental chambers. Their changes in facial expressions were compared between different exposure conditions and the potential correlation between facial expressions and perceived air quality, environmental satisfaction, and self-reported sick building syndrome symptoms was explored. Preliminary results indicate that a common RGB camera can repeatably detect facial expressions from the same subject. Also, results from a subset of five subjects suggest moderate differences in concentration, calmness, contemplation, and tiredness expressions between the two air quality conditions.

Unsupervised Deep Learning for Advanced Forming Limit Analysis in Sheet Metal: A Tensile Test-Based Approach.

An accurate description of the formability and failure behavior of sheet metal materials is essential for an optimal forming process design. In this respect, the forming limit curve (FLC) based on the Nakajima test, which is determined in accordance with DIN EN ISO 12004-2, is a wide-spread procedure for evaluating the formability of sheet metal materials. Thereby the FLC is affected by influences originating from intrinsic factors of the Nakajima test-setup, such as friction, which leads to deviations from the linear strain path, biaxial prestress and bending superposition. These disadvantages can be circumvented by an alternative test combination of uniaxial tensile test and hydraulic bulge test. In addition, the forming limit capacity of many lightweight materials is underestimated using the cross-section method according to DIN EN ISO 12004-2, due to the material-dependent occurrence of multiple strain maxima during forming or sudden cracking without prior necking. In this regard, machine learning approaches have a high potential for a more accurate determination of the forming limit curve due to the inclusion of other parameters influencing formability. This work presents a machine learning approach focused on uniaxial tensile tests to define the forming limit of lightweight materials and high-strength steels. The transferability of an existing weakly supervised convolutional neural network (CNN) approach was examined, originally designed for Nakajima tests, to uniaxial tensile tests. Additionally, a stereo camera-based method for this purpose was developed. In our evaluation, we train and test materials, including AA6016, DX54D, and DP800, through iterative data composition, using cross-validation. In the context of our stereo camera-based approach, strains for different materials and thicknesses were predicted. In this cases, our method successfully predicted the major strains with close agreement to ISO standards. For DX54D, with a thickness of 0.8 mm, the prediction was 0.659 (compared to ISO's 0.664). Similarly, for DX54D, 2.0 mm thickness, the predicted major strain was 0.780 (compared to ISO 0.705), and for AA6016, at 1.0 mm thickness, a major strain of 0.314 (in line with ISO 0.309) was estimated. However, for DP800 with a thickness of 1.0 mm, the prediction yielded a major strain of 0.478 (as compared to ISO 0.289), indicating a divergence from the ISO standard in this particular case. These results in general, generated with the CNN stereo camera-based approach, underline the quantitative alignment of the approach with the cross-section method.

Comparative study of interaction methods for mobile gaming while running on a treadmill

Mobile video games that integrate physical activity, such as treadmill running, offer a unique combination of entertainment and exercise. This study presents a comparative evaluation of interaction methods used in mobile games during treadmill running, focusing on smartphone accelerometers, smartwatch accelerometers, and the smartphone’s front camera. The goal of the research is to test whether the three interaction methods are suitable for use in treadmill-based games. For this purpose, the study aims to understand the strengths and limitations of each method to inform the design of more engaging and efficient mobile running games. The results indicate that all three methods provide acceptable accuracy for a game like the one used in the study. However, the findings highlight that the camera-based interaction method stands out for its immersive experience and reduced reliance on external devices. These findings contribute to the design of future mobile games that integrate physical activity, offering valuable insights for users looking to combine digital enjoyment with physical exercise.

A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects

Visual-based positioning technology plays a pivotal role in spatial artificial intelligence, facilitating precise perception and comprehension of the physical world for robotic platforms and augmented reality devices. In this study, we propose a binocular camera-based method for spatial localization of targets using CNN for instance segmentation while simultaneously providing target location information. The method encompasses image acquisition and correction, target recognition and segmentation, and stereo matching, among other components. Building upon this foundation, we introduce a pedestrian recognition segmentation network model with an attention mechanism. To accurately locate the target, we employ a multi-feature fusion feature point extraction and matching algorithm that combines edge information with semantic information. Finally, our proposed method is evaluated for dynamic pedestrian targets in indoor environments, achieving a horizontal positioning error of less than 0.25 m.

Predictive Value of Camera-based Donor Glomerular Filtration Rate Estimation on the Immediate Renal Allograft Outcome Following Live-related Renal Transplant: A Single-center Retrospective Study.

The purpose of this study was to assess the association of measured glomerular filtration rate (mGFR) using camera-based method with early transplant outcomes. Diethylenetriamine pentaacetate renograms of all voluntary kidney donors between January 2016 and December 2022 at Kasturba Hospital, Manipal, India, were retrieved for the study. Recipients' posttransplant biochemical parameters were collected and compared against donors with scaled mGFR >80 ml/min/1.73 m2 (Group 1) and with mGFR between 60 and 80 ml/min/1.73 m2 (Group 2). Donor-recipient pair age, anthropometric parameters, and their differences were also assessed against the immediate transplant outcome. Posttransplant immediate graft function was assessed by posttransplant nadir serum creatinine, day to achieve nadir serum creatinine, the incidence of slow graft or delayed graft function, and serum creatinine at 1-month posttransplantation. Recipients with serum creatinine of >2.5 mg/dl on posttransplant day 7 were taken as slow graft function. A total of 161 donor-recipient pairs were analyzed in the study. In recipients who showed persistently high serum creatinine posttransplant, older donor age(p < 0.001), higher difference in body mass index among the donor-recipient pair (p= 0.03), and mGFR <80ml/min (p < 0.001) were significantly associated. Slow graft function was significantly more in Group II recipients, with donors having mGFR <80ml/min as compared to Group I with mGFR >80 ml/min (37.3% vs. 10.6%) (P < 0.001). Camera-based mGFR using Gates' formula is a reliable tool to predict inferior graft outcomes in the immediate posttransplant period. Kidneys from donors with mGFR of 60-80 mL/min/1.73 m2 are likely to experience slow graft function in the immediate posttransplant period.

Landmark Based Bronchoscope Localization for Needle Insertion Under Respiratory Deformation.

Bronchoscopy is currently the least invasive method for definitively diagnosing lung cancer, which kills more people in the United States than any other form of cancer. Successfully diagnosing suspicious lung nodules requires accurate localization of the bronchoscope relative to a planned biopsy site in the airways. This task is challenging because the lung deforms intraoperatively due to respiratory motion, the airways lack photometric features, and the anatomy's appearance is repetitive. In this paper, we introduce a real-time camera-based method for accurately localizing a bronchoscope with respect to a planned needle insertion pose. Our approach uses deep learning and accounts for deformations and overcomes limitations of global pose estimation by estimating pose relative to anatomical landmarks. Specifically, our learned model considers airway bifurcations along the airway wall as landmarks because they are distinct geometric features that do not vary significantly with respiratory motion. We evaluate our method in a simulated dataset of lungs undergoing respiratory motion. The results show that our method generalizes across patients and localizes the bronchoscope with accuracy sufficient to access the smallest clinically-relevant nodules across all levels of respiratory deformation, even in challenging distal airways. Our method could enable physicians to perform more accurate biopsies and serve as a key building block toward accurate autonomous robotic bronchoscopy.

Learning based compressive snapshot spectral light field imaging with RGB sensors.

The application of multidimensional optical sensing technologies, such as the spectral light field (SLF) imager, has become increasingly common in recent years. The SLF sensors provide information in the form of one-dimensional spectral data, two-dimensional spatial data, and two-dimensional angular measurements. Spatial-spectral and angular data are essential in a variety of fields, from computer vision to microscopy. Beam-splitters or expensive camera arrays are required for the usage of SLF sensors. The paper describes a low-cost RGB light field camera-based compressed snapshot SLF imaging method. Inspired by the compressive sensing paradigm, the four dimensional SLF can be reconstructed from a measurement of an RGB light field camera via a network which is proposed by utilizing a U-shaped neural network with multi-head self-attention and unparameterized Fourier transform modules. This method is capable of gathering images with a spectral resolution of 10 nm, angular resolution of 9 × 9, and spatial resolution of 622 × 432 within the spectral range of 400 to 700 nm. It provides us an alternative approach to implement the low cost SLF imaging.

Experimental investigation of the tire wear process using camera-assisted observation assessed by numerical modeling

This paper presents a novel experimental method to study the abrasion mechanism of car tires. It is based on the detection of microscopic movements associated with material damage (cracking) on the rubber tread. This is referred to as degrading layer relaxation. It correlates with the wear rate and, interestingly, the direction of the pattern’s movement is opposite to the lateral forces during cornering. To measure and analyze the microscopic movements, a new camera-based method with feature point matching using video stabilization was developed. Besides extensive experimental investigation, the formation and propagation of microcracks are investigated using a simplified numerical model in which a phase field approach coupled with a viscoelastic constitutive behavior is implemented in a finite element framework.

Assessing Different Methods for Detection of Distracted Driving

Roadside observations provide insight into the prevalence of distracted driving on American roadways. Traditional observational studies require the use of observers stationed on the roadside who manually annotate the behavior of passing drivers, while camera-based methods allow researchers to code distracted driving from photos without an observer on the scene. The current study compared the accuracy of a traditional in-person roadside observer with a camera-based method in identifying driver distraction. Both methods distinguished cellphone distractions from other behaviors with high accuracy, although the in-person observer performed slightly better than the camera-based method across most specific behaviors. This study demonstrates that both in-person and camera-based observations are reasonably accurate approaches for measuring the prevalence of cellphone distractions on the road. Camera-based methods may provide an advantage to in-person observers by enabling observations on roadways where it is not feasible to station a human observer.

A camera based method for assessing surf amenity of submerged nearshore structures in a wave basin by quantifying wave breaking

Submerged nearshore structure designs are commonly tested and optimised in scale model wave basins prior to construction in the field. Depending on the purpose of the structure, different measurement methods are used. In this paper, a camera-based measurement method called Wave Peel Tracking (WPT) was adapted and evaluated for quantifying wave breaking over submerged structures purposed for surf amenity. The WPT algorithm was successfully applied to the wave basin through controlled artificial lighting at night, precise image rectification, and modification to a classifier model. The WPT results were verified using overhead footage and had similar accuracy to the WPT algorithm previously applied in the field (Thompson et al., 2021a). Results from wave basin testing of two rock reef structures and three sand mound structures are presented for both regular and irregular waves and were analysed to show the effectiveness of the WPT algorithm in quantifying surf amenity for informing design optimisation. The algorithm applied to the wave basin is open-source, enabling further use and adoption by other laboratories.