Test Video Research Articles

Toward Intelligent Head Impulse Test: A Goggle-Free Approach Using a Monocular Infrared Camera.

To assess vestibular function, video head impulse test (vHIT) is taken as the gold standard by evaluating the vestibulo-ocular reflex (VOR). However, vHIT requires the patient to wear a specialized head-mounted goggle equipment that needs to be calibrated before each use. For this, we proposed an intelligent head impulse test (iHIT) setting with a monocular infrared camera instead of the head-mounted goggle and contributed correspondingly a video classification approach with deep learning to vestibular function determination. Within the iHIT framework, a monocular infrared camera was set in front of the patient to capture test videos, based on which a dataset DiHIT of HIT video clips was set up. We then proposed a two-stage multi-modal video classification network, trained on the dataset DiHIT, that took as input the eye motion and head motion data extracted from the facial keypoints via HIT clips and outputted the identification of the semicircular canal (SCC) being tested (SCC identification) and determination of VOR abnormality (SCC qualitation). Experiments on this dataset DiHIT showed that it achieved the accuracy of 100% in prediction of SCC identification. Furthermore, it attained predictive accuracies of 84.1% in horizontal and 79.0% in vertical SCC qualitation. Compared with existing video-based HIT, iHIT eliminates goggles, does not require equipment calibration, and achieves complete automation. Furthermore, iHIT will bring more benefits to users due to its low cost and ease of operation. Codes and use case pipeline are available at: https://github.com/dec1st2023/iHIT. 3 Laryngoscope, 2024.

Real-Time Tracking Target System Based on Kernelized Correlation Filter in Complicated Areas.

The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target's two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience.

Evaluation of Saccadic Component Measure on Smooth Pursuit Tests.

Despite the advancement of eye-tracking technology for smooth pursuit (SP) eye movement evaluation, qualitative observation offers much information that is not captured by computers; hence, both objective and qualitative information should be utilized to evaluate SP. This study examined the consistency among our clinicians when evaluating SP using normal (N), grossly normal (GN), mildly abnormal (MA), and abnormal (AB) as classifications. We then evaluated the effect of combining GN and MA into a single subclinical (SUBC) category. We also evaluated the computerized percent saccade (PS) metric by determining its sensitivity and specificity in classifying SP. Retrospective horizontal and vertical SP test videos and numerical data for 70 participants were obtained from the Neuro Kinetics Neuro-Otologic Test Center and de-identified. From this, eye-tracking videos, time plots of eye-tracking positional data, and tables of SP eye-tracking performance data were generated for 0.1, 0.3, and 0.5 Hz in both horizontal and vertical planes, totaling 6 tests per subject. Three clinicians rated each subject's SP performance as N, GN, MA, or AB for a total of 6 ratings (3 frequencies, horizontal and vertical). This process was repeated using N, SUBC, and AB as rating categories. Clinicians also provided an overall SP rating for each plane as follows: AB if the results were abnormal for 2 or more frequencies tested. Alternatively, if fewer than 2 frequencies presented with a rating of AB, then an overall rating of MA, GN, or N was determined at the respective clinician's discretion. When the 3 clinicians were tasked with classifying SP videos using 4 clinical categories, fair overall agreement was demonstrated. However, when MA and GN categories were combined into an SUBC category, the overall agreement for the 3 clinicians improved slightly for both horizontal SP (HSP) and vertical SP (VSP). This pattern of agreement did not differ considerably when comparing HSP versus VSP, and good consistency and reliability was observed across clinicians. Again, inter-rater consistency was smaller for VSP versus HSP despite the reduction in clinical categories. Cut-off values were generated for the PS metric and demonstrated good specificity and sensitivity when they were exceeded for 2 or more frequencies in a particular plane when evaluating a subject's SP test.

Tracking and automatic behavioral analysis of group-housed pigs based on YOLOX+BoT-SORT-slim

Automatic analysis of pig behavior is crucial for assessing their health and welfare status in group-housed pig farms. Currently, computer vision technology has been widely applied to recognize and track pig behavior. However, these applications lack the capability for automatic behavior analysis. Additionally, execution speed is crucial for long-term tracking, as the tracking target can easily be lost due to the complex background of pig farms, uneven lighting, and the similar appearance of pigs. To tackle these challenges, we propose a lightweight multi-object tracking (MOT) and behavioral analysis method: YOLOX+BoT-SORT-Slim. Firstly, the YOLOX detector detects pig targets and recognizes the pigs’ four behaviors including “stand”, “lie”, “eat”, and “other”. Secondly, the BoT-SORT-Slim algorithm tracks the detected pigs and their behaviors at high speed. Finally, we devise a behavior automatic analysis algorithm that integrates the behavioral information and the tracking results to complete pigs’ behavior analysis. To evaluate the proposed method, we conducted experiments on 1-minute, 10-minute, and 1-hour datasets. The experimental results show that on the 1-minute public and private test sets, the proposed method achieves 6.4 and 3.6 times the frame rate (FPS) of the original algorithm, with 68.05 FPS and 71.27 FPS, respectively. It also achieves a higher-order tracking accuracy (HOTA) of 84.7 % and 78.7 %, multi-object tracking accuracy (MOTA) of 98.3 % and 97.0 %, and an identification F1 score (IDF1) of 98.4 % and 93.4 %. The proposed method obtains the best results on all test sets compared to five other MOT algorithms including DeepSORT, StrongSORT, ByteTrack, OC-SORT, and BoT-SORT. In the 10-minute and 1-hour test sets, the proposed method achieves 70.28 and 72.24, 63.3 % and 76.4 %, 94.1 % and 99.6 %, and 72.5 % and 90.2 % for FPS, HOTA, MOTA, and IDF1, respectively. In terms of behavior analysis, three types of pigs’ behavior analysis are plotted for each test video to assess their health status. The experimental results indicate that the proposed method performs excellently in behavior analysis and tracking, which can reliably monitor and manage pig behavior in real time, providing automated management technical support.

Fishing event detection and species classification using computer vision and artificial intelligence for electronic monitoring

Fisheries regulations require detailed catch reporting on commercial fishing vessels. Vital components for the sustainable management of fish stocks include a robust estimate of the number of fish caught and the species composition. Catch recording is often done manually by human observers on fishing vessels. Human observers are costly, and consistent data streams can be subject to observer availability and the weather. On-vessel cameras (electronic monitoring, EM) are a growing alternative to human observers. However, on-land human auditors are required to review hundreds of hours of videos recorded during fishing trips that can last for weeks. In this paper, a framework is presented to automatically detect fish in EM videos, count the total fishing events, and classify the fish species. For this purpose, a deep learning and computer vision-based model is developed to efficiently detect fish and fishers onboard a vessel. Secondly, a vision-based tracking pipeline tracks the detected fish and counts the total fishing events in the videos. Thirdly, the extracted fishing events are classified through a deep learning-based fish species classifier, to provide the distribution of different fish species caught for a fishing trip. For our experiments, the datasets were prepared using the electronic monitoring data of multiple fishing trips of a fishing vessel. The videos were recorded on Australian longline vessels targeting tunas and billfish. For the fish detection task, video frames were extracted and labelled manually to provide a digital ground-truth. For the fish species classification task, hundreds of fish images of multiple species were cropped to provide a training dataset for the fish classifier. For the fish counting task, manual counts for the fishing events of individual fish species were generated for the test fishing trips. The developed fish and fisher detector achieves a mean Average Precision of 87.0 % for fish and 94.0 % for fishers on test video frames. The fishing event detection pipeline achieves an Average Precision of 81.0 % and an Average Recall of 74.5 % on test videos. The fish species classifier achieves an Accuracy (Top-1) of 91.11 % for the classification of cropped fish images and 89.05 % for the classification of extracted fishing events from the videos. Experimental results show that our proposed computer vision and artificial intelligence-based solution for video analysis has great potential to automate the auditing process from electronic monitoring footage and contribute to the sustainable management of fish stocks.

Suitability of GPT-4o as an evaluator of cardiopulmonary resuscitation skills examinations

AimTo assess the accuracy and reliability of GPT-4o for scoring examinees’ performance on cardiopulmonary resuscitation (CPR) skills tests. MethodsThis study included six experts certified to supervise the national medical licensing examination (three junior and three senior) who reviewed the CPR skills test videos across 103 examinees. All videos reviewed by the experts were subjected to automated assessment by GPT-4o. Both the experts and GPT-4o scored the videos across four sections: patient assessment, chest compressions, rescue breathing, and repeated operations. The experts subsequently rated GPT-4o’s reliability on a 5-point Likert scale (1, completely unreliable; 5, completely reliable). GPT-4o’s accuracy was evaluated using the intraclass correlation coefficient (for the first three sections) and Fleiss’ Kappa (for the last section) to assess the agreement between its scores vs. those of the experts. ResultsThe mean accuracy scores for the patient assessment, chest compressions, rescue breathing, and repeated operation sections were 0.65, 0.58, 0.60, and 0.31, respectively, when comparing the GPT-4o’s vs. junior experts’ scores and 0.75, 0.65, 0.72, and 0.41, respectively, when comparing the GPT-4o’s vs. senior experts’ scores. For reliability, the median Likert scale scores were 4.00 (interquartile range [IQR] = 3.66–4.33, mean [standard deviation] = 3.95 [0.55]) and 4.33 (4.00–4.67, 4.29 [0.50]) for the junior and senior experts, respectively. ConclusionsGPT-4o demonstrated a level of accuracy that was similar to that of senior experts in examining CPR skills examination videos. The results demonstrate the potential for deploying this large language model in medical examination settings.

A positive scratch collapse test in anterior cutaneous nerve entrapment syndrome indicates its neuropathic character.

Chronic abdominal pain is occasionally caused by an abdominal wall entity such as anterior cutaneous nerve entrapment syndrome (ACNES). This syndrome is thought to occur due to intercostal nerve branches (T7-12) that are entrapped in the rectus abdominis muscles. The diagnosis is largely based on subjective clues in patient history and physical examination. A test referred to as the scratch collapse test (SCT) is used as an additional diagnostic tool in peripheral nerve entrapment syndromes such as the carpal tunnel syndrome. The aim of the present study is to investigate whether an SCT was positive in patients with suspected ACNES. If so, this finding may support its hypothesized neuropathic character. A prospective, case-control study was performed among patients with ACNES (n = 20) and two control groups without ACNES (acute intra-abdominal pathology n = 20; healthy n = 20), all were consecutively included. ACNES was diagnosed based on previously published criteria. The SCT test was executed at the painful abdominal area in both patient groups and at a corresponding area in healthy controls. Predictive values, sensitivity, and specificity were calculated. Videos of tests were evaluated by blinded observers. SCT was judged positive in 19 of 20 ACNES patients but not in any of the 40 controls. A 95% sensitivity (confidence interval [CI]: 75-99) and optimal specificity (100%; CI: 83-100) were calculated. The positive SCT supports the hypothesis that ACNES is an entrapment neuropathy. A positive SCT should be considered a major diagnostic criterion for ACNES.

A New Method for Non-Destructive Identification and Tracking of Multi-Object Behaviors in Beef Cattle Based on Deep Learning.

The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods are time-consuming and labor-intensive, which hinders precise cattle farming. This paper utilizes deep learning algorithms to achieve the identification and tracking of multi-object behaviors in beef cattle, as follows: (1) The beef cattle behavior detection module is based on the YOLOv8n algorithm. Initially, a dynamic snake convolution module is introduced to enhance the ability to extract key features of beef cattle behaviors and expand the model's receptive field. Subsequently, the BiFormer attention mechanism is incorporated to integrate high-level and low-level feature information, dynamically and sparsely learning the behavioral features of beef cattle. The improved YOLOv8n_BiF_DSC algorithm achieves an identification accuracy of 93.6% for nine behaviors, including standing, lying, mounting, fighting, licking, eating, drinking, working, and searching, with average 50 and 50:95 precisions of 96.5% and 71.5%, showing an improvement of 5.3%, 5.2%, and 7.1% over the original YOLOv8n. (2) The beef cattle multi-object tracking module is based on the Deep SORT algorithm. Initially, the detector is replaced with YOLOv8n_BiF_DSC to enhance detection accuracy. Subsequently, the re-identification network model is switched to ResNet18 to enhance the tracking algorithm's capability to gather appearance information. Finally, the trajectory generation and matching process of the Deep SORT algorithm is optimized with secondary IOU matching to reduce ID mismatching errors during tracking. Experimentation with five different complexity levels of test video sequences shows improvements in IDF1, IDS, MOTA, and MOTP, among other metrics, with IDS reduced by 65.8% and MOTA increased by 2%. These enhancements address issues of tracking omission and misidentification in sparse and long-range dense environments, thereby facilitating better tracking of group-raised beef cattle and laying a foundation for intelligent detection and tracking in beef cattle farming.

Three-view cotton flower counting through multi-object tracking and RGB-D imagery

Monitoring the number of cotton flowers can provide important information for breeders to assess the flowering time and the productivity of genotypes because flowering marks the transition from vegetative growth to reproductive growth and impacts the final yield. Traditional manual counting methods are time-consuming and impractical for large-scale fields. To count cotton flowers efficiently and accurately, a multi-view multi-object tracking approach was proposed by using both RGB and depth images collected by three RGB-D cameras fixed on a ground robotic platform. The tracking-by-detection algorithm was employed to track flowers from three views simultaneously and remove duplicated counting from single views. Specifically, an object detection model (YOLOv8) was trained to detect flowers in RGB images and a deep learning-based optical flow model Recurrent All-pairs Field Transforms (RAFT) was used to estimate motion between two adjacent frames. The intersection over union and distance costs were employed to associate flowers in the tracking algorithm. Additionally, tracked flowers were segmented in RGB images and the depth of each flower was obtained from the corresponding depth image. Those flowers tracked with known depth from two side views were then projected onto the middle image coordinate using camera calibration parameters. Finally, a constrained hierarchy clustering algorithm clustered all flowers in the middle image coordinate to remove duplicated counting from three views. The results showed that the mean average precision of trained YOLOv8x was 96.4%. The counting results of the developed method were highly correlated with those counted manually with a coefficient of determination of 0.92. Besides, the mean absolute percentage error of all 25 testing videos was 6.22%. The predicted cumulative flower number of Pima cotton flowers is higher than that of Acala Maxxa, which is consistent with what breeders have observed. Furthermore, the developed method can also obtain the flower number distributions of different genotypes without laborious manual counting in the field. Overall, the three-view approach provides an efficient and effective approach to count cotton flowers from multiple views. By collecting the video data continuously, this method is beneficial for breeders to dissect genetic mechanisms of flowering time with unprecedented spatial and temporal resolution, also providing a means to discern genetic differences in fecundity, the number of flowers that result in harvestable bolls. The code and datasets used in this paper can be accessed on GitHub: https://github.com/UGA-BSAIL/Multi-view_flower_counting.

A dual-instrument Kalman-based tracker to enhance robustness of microsurgical tools tracking.

The integration of a surgical robotic instrument tracking module within optical microscopes holds the potential to advance microsurgery practices, as it facilitates automated camera movements, thereby augmenting the surgeon's capability in executing surgical procedures. In the present work, an innovative detection backbone based on spatial attention module is implemented to enhance the detection accuracy of small objects within the image. Additionally, we have introduced a robust data association technique, capable to re-track surgical instrument, mainly based on the knowledge of the dual-instrument robotics system, Intersection over Union metric and Kalman filter. The effectiveness of this pipeline was evaluated through testing on a dataset comprising ten manually annotated videos of anastomosis procedures involving either animal or phantom vessels, exploiting the Symani®Surgical System-a dedicated robotic platform designed for microsurgery. The multiple object tracking precision (MOTP) and the multiple object tracking accuracy (MOTA) are used to evaluate the performance of the proposed approach, and a new metric is computed to demonstrate the efficacy in stabilizing the tracking result along the video frames. An average MOTP of 74±0.06% and a MOTA of 99±0.03% over the test videos were found. These results confirm the potential of the proposed approach in enhancing precision and reliability in microsurgical instrument tracking. Thus, the integration of attention mechanisms and a tailored data association module could be a solid base for automatizing the motion of optical microscopes.

Fecal calprotectin diagnostic level gradient along the small bowel in patients with Crohn's disease.

Fecal calprotectin (FC) is known to be a sensitive biomarker of colonic inflammation but to a lesser degree of small bowel (SB) inflammation. Moreover, data on FC's diagnostic levels in different SB segments are scarce. We aimed to examine FC's diagnostic levels along the SB-axis in CD. This was a post-hoc aggregated analysis of five prospective studies of adult CD patients, who underwent FC testing and SB video capsule endoscopy (VCE). Lewis score (LS) inflammation in different SB segments was tested for correlation with FC level after exclusion of colonic disease. The diagnostic levels of FC for SB inflammatory topographical-gradient were assessed using a receiver operating characteristic. 214 patients were included (age:30 [24-43] year-old, males-57%). For a similar SB inflammatory-activity (LS≥135), FC levels incrementally increased from proximal to distal SB segments (63 [30-121] versus 190 [78-549], p=0.005) and from distal SB segment to the colon (190 [78-549] versus 542 [185-1000], p=0.010). The best FC cutoffs to identify isolated mild proximal/distal SB-inflammation (LS≥135) were 77μgg and 123μgg, respectively. A cutoff of 234μgg was best to detect more significant proximal inflammation (LS≥350) when only mild distal SB-inflammation was present. In sensitivity analyses, this proximal-to-distal FC gradient was maintained when LS≥350 and LS≥790 were used as the inflammatory reference-values. Unlike FC, the magnitude of CRP elevation was unrelated to the topography of inflammation along the SB-axis. FC may serve as a topographical biomarker of CD-activity, with its sensitivity to identify mucosal inflammation increases from proximal to distal SB segments.

Organizing Graphical User Interface tests from behavior‐driven development as videos to obtain stakeholders' feedback

AbstractDemonstrating software early and responding to feedback is crucial in agile development. However, it is difficult for stakeholders who are not on‐site customers but end users, marketing people, or designers, and so forth to give feedback in an agile development environment. Successful graphical user interface (GUI) test executions can be documented and then demonstrated for feedback. In our new concept, GUI tests from behavior‐driven development (BDD) are recorded, augmented, and demonstrated as videos. A GUI test is divided into several GUI unit tests, which are specified in Gherkin, a semi‐structured natural language. For each GUI unit test, a video is generated during test execution. Test steps specified in Gherkin are traced and highlighted in the video. Stakeholders review these generated videos and provide feedback, for example, on misunderstandings of requirements or on inconsistencies. To evaluate the impact of videos in identifying inconsistencies, we asked 22 participants to identify inconsistencies between (1) given requirements in regular sentences and (2) demonstrated behaviors from videos with Gherkin specifications or from Gherkin specifications alone. Our results show that participants tend to identify more inconsistencies from demonstrated behaviors, which are not in accordance with given requirements. They tend to recognize inconsistencies more easily through videos than through Gherkin specifications alone. The types of inconsistency are threefold: The mentioned feature can be incorrectly implemented, not implemented, or an unspecified new feature. We use a fictitious example showing how this feedback helps a product owner and her team manage requirements. We conclude that GUI test videos can help stakeholders give feedback more effectively. By obtaining early feedback, inconsistencies can be resolved, thus contributing to higher stakeholder satisfaction.

FaceMotionPreserve: a generative approach for facial de-identification and medical information preservation

Telemedicine and video-based diagnosis have raised significant concerns regarding the protection of facial privacy. Effective de-identification methods require the preservation of diagnostic information related to normal and pathological facial movements, which play a crucial role in the diagnosis of various movement, neurological, and psychiatric disorders. In this work, we have developed FaceMotionPreserve , a deep generative model-based approach that transforms patients’ facial identities while preserving facial dynamics with a novel face dynamic similarity module to enhance facial landmark consistency. We collected test videos from patients with Parkinson’s disease recruited via telemedicine for evaluation of model performance and clinical applicability. The performance of FaceMotionPreserve was quantitatively evaluated based on neurologist diagnostic consistency, critical facial behavior fidelity, and correlation of general facial dynamics. In addition, we further validated the robustness and advancements of our model in preserving medical information with clinical examination videos from a different cohort of patients. FaceMotionPreserve is applicable to real-time integration, safeguarding facial privacy while retaining crucial medical information associated with facial movements to address concerns in telemedicine, and facilitating safer and more collaborative medical data sharing.

Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain

AbstractAirport checked luggage entails specific requirements for speed, stability, and reliability. The issue of abnormal retention of checked luggage presents a significant challenge to aviation safety and transportation efficiency. Traditional luggage monitoring systems exhibit limitations in terms of accuracy and timeliness. To address this challenge, this paper proposes a real‐time detection and alerting of luggage anomaly retention based on the YOLOv5 object detection model, leveraging visual algorithms. By eliminating cloud servers and deploying multiple edge servers to establish a private chain, images of anomalously retained luggage are encrypted and stored on the chain. Data users can verify the authenticity of accessed images through anti‐tampering algorithms, ensuring the security of data transmission and storage. The deployment of edge computing servers can significantly reduce algorithm latency and enhance real‐time performance. This solution employs computer vision technology and an edge computing framework to address the speed and stability of checked luggage transportation. Furthermore, blockchain technology greatly enhances system security during operation. A model trained on a sample set of 4600 images achieved a luggage recognition rate of 96.9% and an anomaly detection rate of 95.8% in simulated test videos.

P-203 Training of an artificial intelligence (AI) model to automatically detect nuclear errors during first cleavages

Abstract Study question Can an artificial intelligence (AI) algorithm be developed to automatically detect nuclear errors through dynamic images of first embryonic cleavages? Summary answer The AI model showed an accuracy and precision of above 60% in automatically detecting multinucleation in dynamic images. What is known already Implementation of dynamic monitoring by time-lapse (TL) incubators intends to enhance embryo selection, potentially leveraging algorithmic assistance. During embryonic development, certain morphological nuances, often overlooked in intermittent evaluations, become more apparent through TL monitoring. Some of these features are related to faulty karyokinesis in blastomeres, recognized as nuclear errors, which are linked to aneuploidy and reduced live-birth potential. Despite their significance in embryo selection, existing computer imaging AI models lack the ability to automatically detect these features. This research aims to develop the YOLO AI model’s ability to autonomously identify nuclear errors in dynamic embryonic cleavage imaging. Study design, size, duration Videos of 364 embryos recorded with four different TL incubators (Geri-Genea BiomedX, MIRI-ESCO, EmbryoScope/EmbryoScopePlus-Vitrolife) were randomly sampled from retrospective data. Videos started around the time of pronuclei fading (tPNf) and ceased at t5 (from 20hpi-50hpi). Each frame of each video was annotated by a senior embryologist. A total of 13246 frames were gathered with a resolution of 512x512 pixels, from which 9411 were used to train a YOLO model, 1977 for validation and test phases. Participants/materials, setting, methods For each frame, the embryologist outlined a box around the single nucleus observed in a blastomere and labelled it as “Normal”, and as “Multinucleated” if more than one nucleus or micronuclei were seen. 8% of the training dataset was composed of frames with no visible nucleus and 19% contained at least one multinucleated cell. The model thus further detected the presence of nuclei in a frame and classified it as “Normal” or “Multinucleated” when found. Main results and the role of chance Inferences and metrics were computed at the level of the embryo. No images of test videos were used in training. An embryo was considered “Normal” if no multinucleated cells were detected anywhere during the video. If at least one nuclear error was labelled, the embryo was considered as “Multinucleated”. To get a prediction at the embryo level, we considered the class predicted with the highest confidence during the video by the model. Another rule was to consider the embryo as “Multinucleated” if at least one cell was predicted as “Multinucleated” with a confidence above 0.5. When evaluated on the test dataset, the AI model reached 67% accuracy at detecting multinucleated embryos, with a precision of 63%, correctly identifying 61% of the multinucleated embryos. Conversely, when the model predicted that the embryo did not have any nuclear errors, it was right 61% of the time. Limitations, reasons for caution To reach the current set of results, the present model underwent previous multiple interfaces using several hundreds of embryos, all only trained by a single experienced embryologist. The model must undergo a cross-validation involving embryologists with varying levels of experience to assess its performance with a high degree of precision. Wider implications of the findings Continuous training and refinement of algorithms, informed by the accumulated knowledge of impacting features, play a crucial role in the evolution of automated systems. As these methods evolve, they will further refine embryo selection, improve outcomes, and contribute to the ongoing advancement of IVF. Trial registration number not applicable

Learned fractional downsampling network for adaptive video streaming

Given increasing demand for very large format contents and displays, spatial resolution changes have become an important part of video streaming. In particular, video downscaling is a key ingredient that streaming providers implement in their encoding pipeline as part of video quality optimization workflows. Here, we propose a downsampling network architecture that progressively reconstructs residuals at different scales. Since the layers of convolutional neural networks (CNNs) can only be used to alter the resolutions of their inputs by integer scale factors, we seek new ways to achieve fractional scaling, which is crucial in many video processing applications. More concretely, we utilize an alternative building block, formulated as a conventional convolutional layer followed by a differentiable resizer. To validate the efficacy of our proposed downsampling network, we integrated it into a modern video encoding system for adaptive streaming. We extensively evaluated our method using a variety of different video codecs and upsampling algorithms to show its generality. The experimental results show that improvements in coding efficiency over the conventional Lanczos algorithm and state-of-the-art methods are attained, in terms of PSNR, SSIM, and VMAF, when tested on high-resolution test videos. In addition to quantitative experiments, we also carried out a subjective quality study, validating that the proposed downsampling model yields favorable results.

THE RHETORICAL MODEL OF IELTS SPEAKING TEST BAND 7 OR ABOVE

Rhetorical studies in various texts have been widely conducted. However, compared to studies in written text, spoken text still received very little scholarly attention. For example, studies on rhetorical structure within the International English Language Testing System (IELTS) are hardly found in the literature. Responding to this fundamental need, this study intends to investigate what rhetorical model IELTS test takers employ to make their English-speaking receive excellent scores. This study analyzes ten IELTS speaking tests that receive band seven or above to realize this intention. These ten videos of IELTS speaking tests were taken from YouTube using standard criteria. Their recordings were transcribed to find their rhetorical model, and a top-down analytical approach was employed to find functional moves and strategies to achieve speakers' intentions. The results showed two communicative moves and six strategies as a rhetorical model used by the IELTS test takers who received band seven or above in the present study. This finding implies that understanding the rhetorical model of IELTS speaking may improve awareness of responding to interviewers' requests.

Cost-Effective Ground-Moving Object Detection Method in Aerial Video by Change Detection of Delaunay Triangulation

This paper is dedicated to developing a cost-effective ground-moving object detection method in aerial videos. Without limitations on types, quantity, and distribution of moving objects, which were required by the previous methods, the proposed approach can detect various ground-moving objects in aerial videos captured through diverse flying states in various ground appearances. The proposed method is mainly composed of generation of the appropriate feature points, detection of moving objects, and target tracking. In the originality of our work, a novel detection strategy designed for ground-moving objects is based on change detection of Delaunay triangulation (CDDT) and a three-step motion vector search-based tracking algorithm is further exploited for enhancing the detection rate. Experimental results show that our method can achieve the detection rate of at least 95% (roughly similar to the famous existing state-of-the-art methods) and 0.03 second/frame (far less than the famous existing methods) using test videos (containing only several moving objects distributed in a sparse space) in the previous methods compared. Besides, the average detection rate of 86.81%, average false detection rate of 9.44%, and a frame rate of about 33[Formula: see text]fps can be obtained using our test videos captured in the complicated ground appearances. This result makes the proposed method more attractive for detecting various ground-moving objects in aerial videos, when compared to other approaches, and can also achieve cost-effective performance.

Vision-Based Construction Safety Monitoring Utilizing Temporal Analysis to Reduce False Alarms

Construction safety requires real-time monitoring due to its hazardous nature. Existing vision-based monitoring systems classify each frame to identify safe or unsafe scenes, often triggering false alarms due to object misdetection or false detection, which reduces the overall monitoring system’s performance. To overcome this problem, this research introduces a safety monitoring system that leverages a novel temporal-analysis-based algorithm to reduce false alarms. The proposed system comprises three main modules: object detection, rule compliance, and temporal analysis. The system employs a coordination correlation technique to verify personal protective equipment (PPE), even with partially visible workers, overcoming a common monitoring challenge on job sites. The temporal-analysis module is the key component that evaluates multiple frames within a time window, triggering alarms when the hazard threshold is exceeded, thus reducing false alarms. The experimental results demonstrate 95% accuracy and an F1-score in scene classification, with a notable 2.03% average decrease in false alarms during real-time monitoring across five test videos. This study advances knowledge in safety monitoring by introducing and validating a temporal-analysis-based algorithm. This approach not only improves the reliability of safety-rule-compliance checks but also addresses challenges of misdetection and false alarms, thereby enhancing safety management protocols in hazardous environments.

Innovative graph-based video processing methodology for collapse early warning of historic masonry buildings

In the present study an innovative video processing methodology is proposed for application to vibration data of historic masonry structures. The proposed methodology is based on graph theory and topology analysis applied to magnified videos in search of effective parameters for early-warning signals before the collapse of structures in case of earthquakes. The proposed method was validated through seismic tests of a brick-masonry mockup representing a vault of the mosque in the Palace of the Dey, Algiers. In particular, the seismic tests were carried out at increasing earthquake intensity up to the final collapse of the mockup. After processing the videos of the seismic tests by motion magnification method, the magnified video frames were transformed into a graph of the structure. Finally, several graph indices were calculated and monitored during the vibration. The monitored parameters were analyzed in search of potential threshold values suitable to generate an early warning signal. In particular, the inverse algebraic connectivity provided an early warning signal in the order of a few seconds before collapse. This was validated by comparison with an analogous signal provided at similar time by an accurate displacement lab measurements system based on optical markers positioned at several points of the tested mock-up.