Video Camera Research Articles

Feminist metaphors: Women and lesbians in Portrait of a Lady on Fire

The Portrait of a Lady on Fire is directed by Celine Sciamma and premiered at the Cannes Film Festival on 19 May 2019. The story in this film happens in France in the 18th century when homosexuality was not admitted, and the female social situation is like a commercial good to accomplish their duty for family and wealth. Hence, Celine Sciamma uses some implicit metaphors to release the disadvantaged social status of ladies and to show the ladies' effort to pursue their identity. The metaphors depict the change in the protagonists' inner thoughts. Like the portrait, in the beginning and end of the film, the meaning is different. This paper is based on the scene and plot in The Portrait of a Lady on Fire to analyze how the key metaphors convey the message of feminism and the love between lesbians. By telling the love of lesbians, Celine Sciamma shows that lesbians search for recognition of their homosexual identity and want a homosexual discourse. Feminism is comparatively directly depicted by the camera movement, which gives the power for ladies to gaze at others.

Formation of a panoramic image of the inner surface of the pipe

Background. Visual non-destructive testing of the inner surface of pipes is an important aspect in their production and operation. A defect detected and corrected in a timely manner can significantly reduce the number of defects in production and prevent various emergency incidents during operation. Formation of a complete panoramic image of the inner surface of pipes suitable for quality analysis is an urgent and sought-after task that can be solved using computer vision systems. Aim. This work is the research and development of television methods for forming a complete panoramic image of the inner surface of a pipe, which can be analyzed to search for defects. Methods. To form a cylindrical panoramic image, mathematical models for the formation of an equidistant projection of spherical images obtained using a fisheye lens were used. For high-quality stitching of the resulting frames, digital image processing methods were used, including brightness and contrast transformations, and searching for special points using the MSER algorithm. Theoretical results are verified by full-scale modeling. Results. The result of this work is an algorithm for stitching frames of a video sequence generated by a television camera with a fisheye optical system, uniformly moved along the longitudinal axis of the pipe, into a single panoramic image of the internal surface. Conclusion. The algorithm ensures the formation of a high-quality image of a full panorama of the inner surface of the pipes with the absence of brightness artifacts.

Advancing MARFE detection in JET’s operational camera videos through Machine Learning techniques

In order to prove the capability of operational cameras in nuclear fusion devices, the videos from the cameras at JET were used to detect the occurrence of MARFEs, an edge plasma phenomenon. Three techniques were tested in this work: two already reviewed in other publications and a new one based on intensity masks. Once these methods were validated, their output was used to develop several Machine Learning models to improve performance. A final Machine Learning model was devised using both data from the operational cameras and several signals and diagnostics from other instruments at JET. The outcomes achieved using all the methods presented were deemed satisfactory, leading to the final Machine Learning model exhibiting an impressive accuracy rate of 96.9%. Furthermore, the models allow for detection both in frame by frame (if only video data is used) and in 2 ms time steps should all diagnostics be used.

Student visual attention during group instruction phases in collaborative geometry problem solving

AbstractTeacher gestures support mathematics learning and promote student collaboration. Aligned with speech, gestures can help students to notice the important visual information of geometry tasks. However, students’ visual attention to the teacher’s gestural cues during collaborative problem solving remains a largely unexplored field in mathematics education research. This mixed-method case study investigated relations between students’ visual attention, teacher gestures, and students’ collaborative problem-solving process on a geometry task. The data were collected with video cameras and mobile gaze trackers on four students simultaneously in two Finnish 9th-grade mathematics lessons with the same teacher. The findings show that the students attended to their own papers most of the time during the teacher's gestures, but differences in student attention between the gesture types emerged. The qualitative analysis showed that the teacher’s tracing, pointing, and representational gestures helped in directing student attention to targets relevant to the situational learning process. We conclude that teacher gestures can both convey mathematical contents and direct student attention, which intermediates mathematical thinking in problem solving.

Assessing Table Tennis Technical Proficiency in Individuals with Disabilities: A Scoping Review

Table tennis is a sport that is enjoyed by many, including those with physical and intellectual disabilities. This scoping review summarised the current test protocols for assessing table tennis technical proficiency in individuals with disabilities. Relevant articles were searched through four databases (Scopus, PubMed, SPORTDiscus, and Web of Science) covering three key aspects: disability, table tennis, and technical proficiency. The search resulted in 14 studies included for data extraction, covering physical impairments, intellectual disability, and development coordination disorder. Almost all studies (93%) were conducted on well-trained para-table tennis athletes competing in high-level competitions. There exist protocols to assess service accuracy and stroke accuracy, hand–eye coordination, quality of specific skills and ball control, functional reach, and trunk rotation. The forehand topspin and backhand topspin drives were tested the most. Table tennis robots and video cameras are the common equipment used. Moving forward, future research should develop technical proficiency tests for players across all competency levels. The skill assessment criteria and scoring methods should be standardised and clearly explained. The validity and reliability of tests should be established. Lastly, there is great potential in using artificial intelligence to enhance the assessment of table tennis proficiency in individuals with disabilities.

Non‐extractive fish diversity assessment in Mediterranean rhodolith beds

Abstract Rhodolith beds, recognized as biodiversity hotspots with high ecological and conservation importance, face local anthropic pressures, including trawl fishing. However, monitoring the diversity associated with this sensitive biogenic habitat in the deep waters of the Mediterranean Sea is challenging. Traditional monitoring methods, such as experimental trawling and trammel nets, are extractive and have sampling limitations in providing comprehensive diversity inventories. In this work, we explore three alternative techniques (still video cameras, environmental DNA [eDNA] and bioacoustics) to monitor fish assemblages associated with rhodolith beds in the Menorca Channel at 45–80 m depth. Ten sites were surveyed with eDNA, and 66 fish taxa were identified, including endangered sharks and cryptic species. The molecular operational taxonomic units (MOTUs) richness varied significantly according to habitat heterogeneity, being higher in homogeneous bottoms, and to exposure to trawling, with higher richness in non‐exposed areas. Bioacoustics, though limited by a reduced library of fish sounds, detected differences in sound richness between habitat types, and the highest abundance of sounds was recorded in the areas with the greatest small‐scale habitat heterogeneity. Underwater video revealed limitations in species detection but proved effective for characterizing small‐scale habitat types and heterogeneity. In summary, each technique provides valuable information at different spatial scales and levels of detail. To assess the feasibility of these techniques for long‐term monitoring, we explored their capabilities and limitations in terms of costs, survey logistics and data output.

Eye Strain Expression Classification using Attention Capsule Network for Adapting Screen Vision

Beside the conventional facial expression recognition methods, the research focuses on developing a system for recognizing various eye expressions under different screen conditions. This research deals with the use of Capsule Network (a recent Deep Learning algorithm) to enhance facial expression recognition capabilities and to develop adaptive screen technologies aimed at mitigating digital eye strain. The main objective of this research is to engineer a sophisticated system that employs the capabilities of Capsule Nets to recognize the various expressions that user makes and based on the recognized expression, dynamically modify screen settings, ensuring optimal user visual comfort. The research primarily concentrates on the exploration and application of various Capsule Net architectures designed for the recognition of expressions related to eye strain. The baseline model utilized elementary convolutional layers which feed into subsequent fully connected layers for the task of classification. The model has since been refined by incorporating advanced techniques such as attention mechanisms and more sophisticated network architectures where the classification is done by Capsule Network. Results have demonstrated a modest enhancement in the Capsule Net’s predictive performance, attributed to its superior spatial and hierarchical processing of facial features, in comparison to conventional deep learning approaches. The final model has an accuracy of 82.27%. As a final system the model has been deployed to an application to process frames from video camera in the device and make prediction to prompt the notifications or recommendations.

Unsupervised Monocular Depth Estimation With Channel and Spatial Attention.

Understanding 3-D scene geometry from videos is a fundamental topic in visual perception. In this article, we propose an unsupervised monocular depth and camera motion estimation framework using unlabeled monocular videos to overcome the limitation of acquiring per-pixel ground-truth depth at scale. The photometric loss couples the depth network and pose network together and is essential to the unsupervised method, which is based on warping nearby views to target using the estimated depth and pose. We introduce the channelwise attention mechanism to dig into the relationship between channels and introduce the spatialwise attention mechanism to utilize the inner-spatial relationship of features. Both of them applied in depth networks can better activate the feature information between different convolutional layers and extract more discriminative features. In addition, we apply the Sobel boundary to our edge-aware smoothness for more reasonable accuracy, and clearer boundaries and structures. All of these help to close the gap with fully supervised methods and show high-quality state-of-the-art results on the KITTI benchmark and great generalization performance on the Make3D dataset.

Chelonia mydas detection and image extraction from noisy field recordings

<span lang="EN-US">Wildlife videography is an essential data collection method for conducting research on animals. The video recording process of an animal like the <em>Chelonia Mydas</em> turtle in its natural habitat requires the setting up of special camera traps or by performing complex camera movement to capture the animal in frame whilst the cameraman maneuvers over uneven terrain while filming. The result is hours of footage that only have the presence of the intended subject in it for seconds whilst the rest is background footage; or noisy and blurry footage that has only several usable frames among thousands of noisy and unusable ones. This presents a problem that deep learning models can help to assist, especially in detecting a wildlife subject and extracting usable data from hours of noise and background footage. This paper proposes the use of machine learning models to detect and extract wildlife images of <em>Chelonia Mydas</em> turtles to help prune through hundreds and thousands of frames from several video footages. Our paper shows that utilizing a custom model with various confidence scores can label and crop out images in noisy field video recordings of </span><em><span lang="EN-US">Chelonia Mydas </span></em><span lang="EN-US">turtles with up to 99.89% of output images correctly cropped and labeled.</span>

이동용 영상촬영기기 바디캠을 활용한 팀단위 심폐소생술의 교육피드백 효과

This study conducted a team-based CPR simulation with 32 fourth-year emergency rescue students to determine the effectiveness of training feedback using body cameras used at emergency rescue sites, and measured awareness, training feedback effectiveness, and satisfactio+n before and after body camera feedback. , preferences and difficulties in using body camera devices were identified. Data analysis was performed using SPSS 27.0 program, including descriptive statistics, frequency analysis, paried t-test, and Wilcoxon signed rank test. As a result of the study, the perception of body camera use showed a positive change from 3.73±0.62 points to 4.45±0.54 points, and a positive satisfaction level of 3.98±0.51 was shown (p<.001). Additionally, there was a significant increase in self-check accuracy and performance score after body camera feedback (p<.001). Therefore, during team-based simulation resuscitation training, positive feedback effects in improving self-inspection ability and performance can be achieved by watching body camera videos and using self-checklists without direct feedback from the instructor.

A multimodal analysis of the English-to-Arabic adaptation of Disney’s “The Bare Necessities”

Abstract Due to the diverse roles that songs play in films and the growing need for movie distribution in various languages and cultures, the challenge of translating film songs from one language to another has become formidable. This research aims to apply the multimodal three-dimensional model proposed by Cui and Wang (2022. Film song translation: Verbal, vocal, and visual dimensions: On the Chinese translation of Amazing Grace in the film Forever Young. Babel 68(4). 565–585) to analyze the Arabic dubbed version of the 2016 film song “The Bare Necessities.” The analysis revealed that, verbally, the Arabic dubbed version of the song maintained the gay emotional tone and length but made adaptations to the information, metaphors, and images used in the source. Vocally, similar to the English source song, the Arabic dubbed version did not adhere to 1 m and has a 4/4 time signature, aligning with the catchy melody and reinforcing the song’s theme of embracing simplicity and savoring life’s essentials effortlessly. Finally, visually, the analysis showed that the Arabic dubbed version matches the plot, characters and background pictures on the screen. The study contributed by incorporating a visual analysis of songs, adding camera elements, namely camera movement and shot size, to gain a better understanding of the scenes under analysis. The use of over-the-shoulder shots created a sense of conversation that complements the duet, and the pan right and left during dancing added a vividness that matches the verbal gay tone and vocal lightness of the song. This finding, considering visual camera elements, can be regarded as a suggested revision to the original multimodal framework introduced by Cui and Wang. Further implications are discussed.

Visual Fall Detection Analysis Through Computer Vision and Deep Learning – Technology Proposition

Advances in modern medicine has increased humans’ life span. Orderly adults face mobility problems while aging. They feel less fit to continue any activity for short intervals too. This is due to declining fitness levels or muscle strength, diminished dexterity, and loss of balance. These symptoms lead to the fall of the individual and sometimes fatal too, if immediately not attended to. It’s an alarming issue for people staying alone. They may pose significant health risks and need immediate assistance. Fall detection technologies are majorly categorised as wearable sensors and ambient sensors. Fall detection wearable devices like pendant necklaces, watches and wristband devices, and clip-on medical alerts use accelerometers to detect rapid downward movements that can indicate a fall. They often also include manual alert buttons, for an increased accuracy. This requires technology comfort and awareness for usage. Ambient home sensors use video cameras to monitor the user’s movement and detect falls. When the fall is transmitted to a monitoring center, a representative typically will call the user to check on them before notifying contacts or calling for emergency services, but this can depend on the user’s preferences and risk factors. In this paper we propose a technology, using security cameras to record videos and create a video-based fall detection system. The system uses computer vision and deep learning algorithms to accurately recognize fall-related movements and distinguish them from regular activities. This system can be integrated to prompt alerts to emergency contacts, thus assisting in providing immediate aid to individuals who have experienced a fall. For higher accuracy, multiple-angle videos and multi-person tracking is integrated in this system to estimate the intensity of the fall for immediate attention. Thus, this fall detection system can contribute to the safety, well-being and independence of individuals at risk of falling.

Image moment-based visual positioning and robust tracking control of ultra-redundant manipulator

Image moment features can describe more general target patterns and have good decoupling properties. However, the image moment features that control the camera’s rotation motion around the x-axis and y-axis mainly depend on the target image itself. In this paper, the ultra-redundant manipulator visual positioning and robust tracking control method based on the image moments are advocated.First, six image moment features used to control camera motion around the x-axis and around the y-axis are proposed. And then, a novel method is proposed to use to select image features. For tracking a moving target, a kalman filter combined with adaptive fuzzy sliding mode control method is proposed to achieve tracking control of moving targets, which can estimate changes in image features caused by the target’s motion on-line and compensate for estimation errors. Finally, the experimental system based on Labview-RealTime system and ultra-redundant manipulator is used to verify the real-time performance and practicability of the algorithm. Experimental results are presented to illustrate the validity of the image features and tracking method.

Microsaccade-inspired event camera for robotics.

Neuromorphic vision sensors or event cameras have made the visual perception of extremely low reaction time possible, opening new avenues for high-dynamic robotics applications. These event cameras' output is dependent on both motion and texture. However, the event camera fails to capture object edges that are parallel to the camera motion. This is a problem intrinsic to the sensor and therefore challenging to solve algorithmically. Human vision deals with perceptual fading using the active mechanism of small involuntary eye movements, the most prominent ones called microsaccades. By moving the eyes constantly and slightly during fixation, microsaccades can substantially maintain texture stability and persistence. Inspired by microsaccades, we designed an event-based perception system capable of simultaneously maintaining low reaction time and stable texture. In this design, a rotating wedge prism was mounted in front of the aperture of an event camera to redirect light and trigger events. The geometrical optics of the rotating wedge prism allows for algorithmic compensation of the additional rotational motion, resulting in a stable texture appearance and high informational output independent of external motion. The hardware device and software solution are integrated into a system, which we call artificial microsaccade-enhanced event camera (AMI-EV). Benchmark comparisons validated the superior data quality of AMI-EV recordings in scenarios where both standard cameras and event cameras fail to deliver. Various real-world experiments demonstrated the potential of the system to facilitate robotics perception both for low-level and high-level vision tasks.

Associations between Stress Mindset and Changes in Positive Affect during a Speech Task: A Preliminary Study

A stress mindset is an individual’s belief that stress has either enhancing (stress-is-enhancing mindset: SEM) or debilitating (stress-is-debilitating mindset: SDM) consequences. This study examined the associations between SEM and changes in positive affect during a speech task using a sample of 32 Japanese college students as participants. It was hypothesized that participants with SEM would show a greater increase in positive affect over time than those with SDM. The participants rested for three minutes, prepared for three minutes, delivered a speech about themselves in front of an evaluator and video camera (task period), and again rested for three minutes. Positive affect was measured immediately after the two resting periods and during the speech. The participants were divided into groups of 17 and 15 in the SEM and SDM groups, respectively, based on their stress mindset scores. Both groups were fairly matched concerning age and male/female ratio. Change patterns of positive affect did not differ between the two groups, which did not support the hypothesis. However, positive affect was significantly higher in the SEM group compared with the SDM group. The present results differed from previous findings reported in North America. Inconsistent results are discussed related to differences in the speech task and cross-cultural variations of happiness and coping with stressors between North America and Japan.

Development of an ICT Laparoscopy System with Motion-Tracking Technology for Solo Laparoscopic Surgery: A Feasibility Study

The increasing demand for laparoscopic surgery due to its cosmetic benefits and rapid post-surgery recovery is juxtaposed with a shortage of surgical support staff. This juxtaposition highlights the necessity for improved camera management in laparoscopic procedures, encompassing positioning, zooming, and focusing. Our feasibility study introduces the information and communications technology (ICT) laparoscopy system designed to aid solo laparoscopic surgery. This system tracks a surgeon’s body motion using a controller, manipulating an embedded camera to focus on specific surgical areas. It comprises a camera module, a camera movement controller, and a motor within the main body, operating connected wires according to controller commands for camera movement. Surgeon movements are detected by an inertial measurement unit (IMU) sensor, facilitating precise camera control. Additional features include a foot pedal switch for motion tracking, a dedicated trocar for main body stability, and a display module. The system’s effectiveness was evaluated using an abdomen phantom model and animal experimentation with a porcine model. The camera responded to human movement within 100 ms, a delay that does not significantly affect procedural performance. The ICT laparoscopy system with advanced motion-tracking technology is a promising tool for solo laparoscopic surgery, potentially improving surgical outcomes and overcoming staff shortages.

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%.

Exploring morphometric transformations in temperature-sensitive NiTi instruments alongside real-time intracanal temperature dynamics.

This study was twofold: (i) it aimed to investigate the morphometric changes of three temperature-sensitive nickel-titanium (NiTi) instruments at different temperatures, and (ii) to conduct an invivo real-time analysis of intracanal temperature changes. Changes in the shape and length of XP-Endo Shaper, XP-Endo Finisher, and XP-Endo Finisher-R were evaluated in real time whilst heated in a temperature-controlled water bath from 22 to 45°C. Instruments were fixed to a laminated water-resistant 1 mm graph paper attached to a stone block. Instruments were imaged whilst subjected to increasing temperature using a digital camera attached to an operating microscope. From recorded videos, still frames were extracted at 10-s intervals and changes in the length and shape of each instrument were measured and changes were plotted against time. Moreover, the intracanal temperature of distal roots of lower molars was measured invivo for patients attending the clinic for non-surgical root canal treatments. The temperature was measured using a K-type thermocouple probe inserted into the mid-root level after irrigating the canal with a solution set at room temperature (22°C) or heated to 45°C. The intraoral and intracanal temperatures were recorded using a video camera for 180 s at 5-s intervals to plot the change in the intraoral and intracanal temperature, after both irrigation solution temperatures, with time. The shape transformation of XP-Endo Shaper began at 31.5 ± 2.0°C and reached its optimal transformation at 35.1 ± 1.0°C. For the Finisher and Finisher-R, shape transformations began at 29.2 ± 1.9 and 26.9 ± 2.2°C reaching the optimal transformation at 33.9 ± 1.4 and 32.7 ± 1.7°C, respectively. The average decreases in lengths of XP-Endo Shaper, Finisher, and Finisher-R after full transformation were 0.43 ± 0.23, 1.07 ± 0.22, and 1.15 ± 0.22 mm, respectively. The intracanal temperature reached 32.9 ± 0.8 and 33.2 ± 1.0°C after 3 min of application of irrigation solutions set at 22 or 45°C, respectively. The tested instruments exhibited diverse changes in their shapes and lengths at varying temperatures. Despite the temperature of the irrigation solution, the intracanal temperature consistently remained lower than the intracanal temperature once equilibrium was reached. This highlights the importance of considering the temperature of irrigation solution during invitro testing of endodontic instruments.

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

Facial Feature-Based Attention Tracking System for Enhanced Online Learning Engagement

Recognizing and enhancing student engagement is crucial for improving learning outcomes, particularly in the context of online classes where monitoring can be challenging. Traditional methods of attendance tracking, such as calling out names, are impractical and susceptible to manipulation in the virtual environment. Students might appear 'online' without actively participating, and the absence of video feeds makes it difficult for teachers to verify attendance and attention. In order to realize a highly efficient and robust attendance management and engagement level prediction system for online learning, In the proposed System, the learner’s face is monitored by a video camera while attending a video lecture. Facial features were analyzed to predict reaction time (RT) to a task-irrelevant stimulus, which was assumed to be an index of the level of attention. Then apply a machine learning method, light Gradient Boosting Machine (LightGBM), to estimate RTs from facial features extracted as action units (AUs) corresponding to facial muscle movements by an open-source software (OpenFace). This project is to develops a user-friendly system integrated with private online learning and attendance recording system for teachers that can automatically record students ‘engagement state and attendance then generate attendance reports for online classrooms. It encompasses a novel design using the AI based FFCNN (Face Fiducial Convolution Neural Network) model to capture face biometric randomly from students’ video stream and record their attendance automatically. This integrated solution not only streamlines attendance management but also provides valuable insights into students' engagement levels through facial feature analysis.