Actual Video Research Articles

The Current State of Vascular Surgery Presence in Bilibili Video Platform of China.

BackgroundWith the development of the Internet, more and more patients search for disease-related information on video platforms during the treatment process, and physicians also look for learning materials through these video platforms. Bilibili is one of the most popular video platforms in China. This study evaluated information on various interesting topics, and related surgical procedures searched through Bilibili.MethodThe Bilibili platform was independently queried for 12 common vascular diseases or related surgical procedures between October and November 2021 by two independent authors using the Baidu search engine. Information about the video and uploader was collected, and descriptive analyses of the overall and first-page results were performed.ResultsA total of 3,998 search results were retrieved by searching 12 vascular-related topics, of which 2,225 actual videos (55.7%) were finally confirmed to be related to medicine. Videos for the public accounted for 84.8% of these 2,225 videos. In addition, 50.5% of the video results were uploaded by vascular surgeons, 12.4% by other specialties, 17.7% by organizations, and 19.4% by other individuals. The total number of videos searched for varicose vein and peripheral vascular diseases was the largest, and the total number of leg amputation videos was the smallest. The largest number of videos for medical professionals was about pulmonary embolism, and the smallest was about leg amputation. On the first pages, 168 results (70.0%) were actually medically relevant, and only 7.7% of the videos were uploaded by vascular surgeons.ConclusionOn the Bilibili platform, videos about vascular diseases are extensive but not comprehensive. The videos uploaded by vascular surgeons are rare, and the results searched are not precise. The online presence of vascular surgeons needs to be improved, which may partially solve the problem of low-quality videos due to the lack of strict management and censorship.

Do Patients Continue to Use Video Visits? Factors Related to Continued Video Visit Use.

Many studies have assessed the factors associated with overall video visit use during the COVID-19 pandemic, but little is known about who is most likely to continue to use video visits and why. The authors combined a survey with electronic health record data to identify factors affecting the continued use of video visit. In August 2020, a stratified random sample of 20,000 active patients from a large health care system were invited to complete an email survey on health care seeking preferences during the COVID. Weighted logistic regression models were applied, adjusting for sampling frame and response bias, to identify factors associated with video visit experience, and separately for preference of continued use of video visits. Actual video visit utilization was also estimated within 12 months after the survey. Three thousand three hundred fifty-one (17.2%) patients completed the survey. Of these, 1208 (36%) reported having at least 1 video visit in the past, lowest for African American (33%) and highest for Hispanic (41%). Of these, 38% would prefer a video visit in the future. The strongest predictors of future video visit use were comfort using video interactions (odds ratio [OR] = 5.30, 95% confidence interval [95% CI]: 3.57-7.85) and satisfaction with the overall quality (OR = 3.94, 95% CI: 2.66-5.86). Interestingly, despite a significantly higher satisfaction for Hispanic (40%-55%) and African American (40%-50%) compared with Asian (29%-39%), Hispanic (OR = 0.46, 95% CI: 0.12-0.88) and African American (OR = 0.54, 95% CI: 0.16-0.90) were less likely to prefer a future video visit. Disparity exists in the use of video visit. The association between patient satisfaction and continued video visit varies by race/ethnicity, which may change the future long-term video visit use among race/ethnicity groups.

Time-Lag Aware Latent Variable Model for Prediction of Important Scenes Using Baseball Videos and Tweets

In this study, a novel prediction method for predicting important scenes in baseball videos using a time-lag aware latent variable model (Tl-LVM) is proposed. Tl-LVM adopts a multimodal variational autoencoder using tweets and videos as the latent variable model. It calculates the latent features from these tweets and videos and predicts important scenes using these latent features. Since time lags exist between posted tweets and events, Tl-LVM introduces the loss considering time lags by correlating the feature into the loss function of the multimodal variational autoencoder. Furthermore, Tl-LVM can train the encoder, decoder, and important scene predictor, simultaneously, using this loss function. This is the novelty of Tl-LVM, and this work is the first end-to-end prediction model of important scenes that considers time lags to the best of our knowledge. It is the contribution of Tl-LVM to realize high-quality prediction using latent features that consider time lags between tweets and multiple corresponding previous events. Experimental results using actual tweets and baseball videos show the effectiveness of Tl-LVM.

The Result of the Critical View of Safety Timeout Technique in Prevention of Bile Duct Injury in Laparoscopic Cholecystectomy: A Retrospective Study at a Large Community Teaching Hospital

Background: Laparoscopic cholecystectomy (LC) is the standard treatment for cholelithiasis. However, despite improvements in technology and implementation of a safer approach, such as the critical view of safety (CVS) technique, the risk of bile duct injury (BDI) remains an issue. Objective: To review LC cases at a large community hospital for BDIs and compare BDI rates between conventional technique and CVS timeout approaches. The authors hypothesized that there would be lower BDI rates in the CVS timeout group. Materials and Methods: The authors conducted a retrospective study of 1,033 consecutive patients that underwent LC at the authors’ institution between November 2015 and November 2018. Based on the timeout document and operative reports, the authors classified patients into CVS timeout and no CVS (noCVS) timeout groups and compared the BDI rates and other relevant clinical outcomes. Results: Among the 1,033 patients, 635 and 398 patients were in the noCVS timeout and CVS timeout groups, respectively. There was no significant difference in the incidence of BDI between the two groups at 1.1% versus 1.8%, respectively (p=0.375). The CVS group exhibited less preemptive conversion to open cholecystectomy, lower incidence of uncontrolled intraoperative bleeding, shorter operative time, and shorter length of hospital stay than the noCVS group. In multivariate regression analysis, operative time of more than 90 minutes was the significant independent risk factor associated with BDI, while previous biliary inflammation was marginally associated with BDI. Conclusion: The present study failed to demonstrate the beneficial effect of CVS timeout in preventing the development of BDI. The reasons could be attributed to surgeons’ competency with their varying skills and experience, the occurrence of injury before timeout, the case selection biases, or the wrong CVS identification. Further review of the actual surgical videos may offer more clarity into the unexpected outcome. Keywords: Bile duct injuries; Critical view of safety; Infundibular techniques; Laparoscopic cholecystectomy

Nuclear radiation detection based on the convolutional neural network under public surveillance scenarios

Abstract Nuclear energy is a clean and popular form of energy, but leakage and loss of nuclear material pose a threat to public safety. Radiation detection in public spaces is a key part of nuclear security. Common security cameras equipped with complementary metal oxide semiconductor (CMOS) sensors can help with radiation detection. Previous work with these cameras, however, required slow, complex frame-by-frame processing. Building on the previous work, we propose a nuclear radiation detection method using convolution neural networks (CNNs). This method detects nuclear radiation in changing images with much less computational complexity. Using actual video images captured in the presence of a common Tc-99m radioactive source, we construct training and testing sets. After training the CNN and processing our test set, the experimental results show the high performance and effectiveness of our method.

VideoTrain++: GAN-based adaptive framework for synthetic video traffic generation

Video streaming traffic has been dominating the global network and the challenges have exacerbated with the gaining popularity of interactive videos, a.k.a.360° videos, as they require more network resources. However, effective provision of network resources for video streaming traffic is problematic due to the inability to identify video traffic flows through the network because of end-to-end encryption. Despite the promise given for network security and privacy, end-to-end encryption also provides a shield for adversaries. To this end, encrypted traffic classification and content fingerprinting with advanced Machine Learning (ML) methods have been proposed. Nevertheless, achieving high performance requires a significant amount of training data, which is a challenging task in operational networks due to the sheer volume of traffic and privacy concerns. As a solution, in this paper, we propose a novel Generative Adversarial Network (GAN) based data generation solution to synthesize video streaming data for two different tasks, 360°/normal video classification and video fingerprinting. The solution consists of a percentile-based data mapping mechanism to enhance the data generation process, which is further supported by novel algorithms for data pre-processing and GAN model training. Taking over 6600 actual video traces and generating over 150,000 new traces, our ML-based traffic classification results show a 5%–16% of accuracy improvement in both tasks.

Faces of Tradition in Chinese Performing Arts

Faces of Tradition in Chinese Performing Arts

A New Method for Surface-to-Air Video Detection and Tracking of Airborne Vehicles

Detection process of airborne targets may be thought simple because of the incompatible nature of aircraft, choppers, UAVs, and drones regarding clear sky background. When changes in the background are considered, brightness variation of the sky complicates the process. Changes in the shapes and types of clouds add another challenge to the process. Tracking process directly depends on the detection process and type of the data stream. The practical systems used for video detection and tracking of airborne targets are manual, and manual structures have some drawbacks compared to automatic structures. For video surveillance, guidance, regional security, and defense applications in dense environments, automatic detection and tracking process may be an obligation rather than preference. In this study, an automatic detection and tracking algorithm for video streams of airborne targets is proposed. A land-based moving camera captures the video data, and not only the flying objects but probably also the camera are in motion. Although the detection and tracking of moving objects via moving sensors is a relatively arduous task, this is the prevalent case in real-life scenarios. Video detection and tracking systems have one or more moving video sensors, while one or more flying air vehicles are in operation area. The proposed algorithm includes an image processing stage for detection and a tracking stage for initiation and continuation. An assessment study has been conducted for the actual video data and found that the proposed method yields successful results for detection, track formation, and continuation processes.

Comparative Analysis of Keyframe Extraction Techniques for Video Summarization

Background: With the technological advancement, the quality of life of a human were improved. Also with the technological advancement large amount of data were produced by human. The data is in the forms of text, images and videos. Hence there is a need for significant efforts and means of devising methodologies for analyzing and summarizing them to manage with the space constraints. Video summaries can be generated either by keyframes or by skim/shot. The keyframe extraction is done based on deep learning based object detection techniques. Various object detection algorithms have been reviewed for generating and selecting the best possible frames as keyframes. A set of frames were extracted out of the original video sequence and based on the technique used, one or more frames of the set are decided as a keyframe, which then becomes the part of the summarized video. The following paper discusses the selection of various keyframe extraction techniques in detail. Methods : The research paper is focused at summary generation for office surveillance videos. The major focus for the summary generation is based on various keyframe extraction techniques. For the same various training models like Mobilenet, SSD, and YOLO were used. A comparative analysis of the efficiency for the same showed YOLO giving better performance as compared to the others. Keyframe selection techniques like sufficient content change, maximum frame coverage, minimum correlation, curve simplification, and clustering based on human presence in the frame have been implemented. Results: Variable and fixed length video summaries were generated and analyzed for each keyframe selection techniques for office surveillance videos. The analysis shows that he output video obtained after using the Clustering and the Curve Simplification approaches is compressed to half the size of the actual video but requires considerably less storage space. The technique depending on the change of frame content between consecutive frames for keyframe selection produces the best output for office room scenarios. The technique depending on frame content between consecutive frames for keyframe selection produces the best output for office surveillance videos. Conclusion: In this paper, we discussed the process of generating a synopsis of a video to highlight the important portions and discard the trivial and redundant parts. First, we have described various object detection algorithms like YOLO and SSD, used in conjunction with neural networks like MobileNet to obtain the probabilistic score of an object that is present in the video. These algorithms generate the probability of a person being a part of the image, for every frame in the input video. The results of object detection are passed to keyframe extraction algorithms to obtain the summarized video. From our comparative analysis for keyframe selection techniques for office videos will help in determining which keyframe selection technique is preferable.

Многофункциональная система безопасности угольных шахт – визуализация событий (горнотехнических процессов) с рабочего места шахтера

The paper reviews the use of mobile video monitoring equipment in coal mines. The most common option is the use of stationary video cameras with real-time video streaming to the mine dispatcher's control monitor via cables. Despite all the benefits of the information obtained, this method has certain limitations due to the specific features of the mine atmosphere, i.e. high humidity and dust levels, as well as the impossibility to organize video monitoring over the entire length of the mine workings. Therefore, mobile video monitoring equipment, both portable and vehicle-based, is efficient supplement to the stationary video cameras. The portable devices include smart phones and the battery-powered head lights with an integrated video camera, which have recently become very popular. In both cases, an important consideration, in addition to the actual video capturing, is the issue of transmitting video data to the top level, i.e. to the mine dispatcher's control panel. The following options are possible: connection to the mine wireless network hotspots via radio channel, reading the information in the lamp rooms when leaving the mine and real-time broadcasting from the mine to the top level. The assumption is made that in order to implement the fastest (and the most efficient) way that works without delays between capturing and transmitting of video data to the daylight surface, such as the latter of the options above, a communications infrastructure based on wireless and cable networks needs to be deployed in the mine workings. The required infrastructure is present in a number of systems designed to locate miners inside the mine workings as part of a multifunctional security system, which enables continuous radio communication of individual devices with infrastructure nodes and, therefore, real-time video data transmission.

Screencast-O-Matic to Support EFL Teaching and Learning Amidst the COVID-19 Pandemic

The COVID-19 situation causes a sudden and unexpected shift of face-to-face pedagogy to emergency remote teaching mode. In that transition, teachers often do not have enough space and time to explore various technology to support their teaching and learning practices that should be done flexibly in multiple places and times. In response, the primary purposes of this article are to introduce a technology tool called Screencast-O-Matic (SOM), its potentials, and possible practices of using SOM that EFL lecturers might adopt for their teaching and learning purposes, specifically in the current pandemic era. Some of the practices discussed in this paper are the use of SOM to: record a video lecturer on how to write a paper following an APA format, give video-based feedback to students’ work, make a digital video presentation, and be a digital mirror that helps students review their speaking performance before they do an actual video presentation. The discussions of the paper are supported by some of the authors’ actual experiences in using SOM in their classrooms, related research findings, and literature. Ideas for future research are presented.

Design and ARM-Based Implementation of Bitstream-Oriented Chaotic Encryption Scheme for H.264/AVC Video.

In actual application scenarios of the real-time video confidential communication, encrypted videos must meet three performance indicators: security, real-time, and format compatibility. To satisfy these requirements, an improved bitstream-oriented encryption (BOE) method based chaotic encryption for H.264/AVC video is proposed. Meanwhile, an ARM-embedded remote real-time video confidential communication system is built for experimental verification in this paper. Firstly, a 4-D self-synchronous chaotic stream cipher algorithm with cosine anti-controllers (4-D SCSCA-CAC) is designed to enhance the security. The algorithm solves the security loopholes of existing self-synchronous chaotic stream cipher algorithms applied to the actual video confidential communication, which can effectively resist the combinational effect of the chosen-ciphertext attack and the divide-and-conquer attack. Secondly, syntax elements of the H.264 bitstream are analyzed in real-time. Motion vector difference (MVD) coefficients and direct-current (DC) components in Residual syntax element are extracted through the Exponential-Golomb decoding operation and entropy decoding operation based on the context-based adaptive variable length coding (CAVLC) mode, respectively. Thirdly, the DC components and MVD coefficients are encrypted by the 4-D SCSCA-CAC, and the encrypted syntax elements are re-encoded to replace the syntax elements of the original H.264 bitstream, keeping the format compatibility. Besides, hardware codecs and multi-core multi-threading technology are employed to improve the real-time performance of the hardware system. Finally, experimental results show that the proposed scheme, with the advantage of high efficiency and flexibility, can fulfill the requirement of security, real-time, and format compatibility simultaneously.

Association Loss and Self-Discovery Cross-Camera Anchors Detection for Unsupervised Video-Based Person Re-Identification

With the continuous improvement and development of cameras network, surveillance video has become the data source of the column stream, which greatly promotes the development of cross-camera person re-identification (Re-ID). However, supervised learning requires a lot of effort to manually label cross-cameras pairwise training data, which is lack of scalability and practical in actual video surveillance because there is a lack of well-labeled pairs of positive and negative samples under each camera. For addressing these negative effects, we set judgment conditions by using the association ranking method to self-discover positive and negative track-lets pairs of anchors with none of the pairwise ID labels, thereby defining a triplet loss. In order to optimize association loss for learning effective discriminative feature, the triplet loss adds adaptive weights according to the degree of easy-hard samples to generate an Adaptive Weighted Conditional Triplet Loss. Besides, for increasing the accuracy of self-discovering cross-camera anchors independently, which means successfully mine mutually best-matched track-lets and merge them under cross-camera, we use the top-rank from the intra-camera ranking list as a self-matched query sample which can double verify the matched-degree between top-rank. And eventually, we establish a new Association Loss and Self-Discovery Learning (ALSL) model with a complete end-to-end manner. We use three standard datasets, PRID2011, iLIDS-VID and MARS, to train the model and the experimental results prove that ALSL rank-1 is better than some superior video-based unsupervised person Re-ID methods.

Detection and tracking for the awareness of surroundings of a ship based on deep learning

Abstract To prevent maritime accidents, it is crucial to be aware of the surrounding environment near ships. The images recorded by a camera mounted on a ship could be used for the awareness of other ships surrounding it. In this study, ship awareness was performed using three procedures: detection, localization, and tracking. Initially, ship detection was performed using the deep learning-based detection model, YOLO (You Only Look Once) v3, based on the camera image. A virtual image dataset was constructed using Unity to overcome the difficulty of obtaining camera images onboard with various sizes of ships, and to improve the detection performance. This was followed by the localization procedure in which the position of the horizon on the image was calculated using the orientation information from the ship. Subsequently, the position of the detected ship in the spatial coordinate system was calculated using the horizon information. Following this, the position, course over ground, and speed over ground of the target ships were tracked in the time domain using the extended Kalman filter. A deep learning model that determines the heading of the ship in the image was proposed to utilize abundant information of cameras, and it was used to set the initial value of the Kalman filter. Finally, the proposed method for the awareness of ships was validated using an actual video captured from a camera installed on an actual ship with various encountering scenarios. The tracking results were compared with actual automatic identification system data obtained from other ships. As a result, the entire detection, localization, and tracking procedures showed good performance, and it was estimated that the proposed method for the awareness of the surroundings of a ship, based on camera images, could be used in the future.

Towards Personalised Gaming via Facial Expression Recognition

In this paper we propose an approach for personalising the space in which a game is played (i.e., levels) dependent on classifications of the user's facial expression — to the end of tailoring the affective game experience to the individual user. Our approach is aimed at online game personalisation, i.e., the game experience is personalised during actual play of the game. A key insight of this paper is that game personalisation techniques can leverage novel computer vision-based techniques to unobtrusively infer player experiences automatically based on facial expression analysis. Specifically, to the end of tailoring the affective game experience to the individual user, in this paper we (1) leverage the proven InSight facial expression recognition SDK as a model of the user's affective state InSight, and (2) employ this model for guiding the online game personalisation process. User studies that validate the game personalisation approach in the actual video game Infinite Mario Bros. reveal that it provides an effective basis for converging to an appropriate affective state for the individual human player.

Motion recognition technology of badminton players in sports video images

Intelligent identification technology has made great progress in transportation, and its application in sports has attracted widespread attention. This research mainly discusses the research of human motion recognition technology in sports dance video images. In geometric algebraic space, using instance templates, a new cfrdF method, using css-based similarity of human body features as features, to construct an angle-adaptive and continuous-scale space template matching algorithm to calculate the similarity between horizontal plates and detected images value, set a certain threshold, so as to match the area where the human body is located. Based on the actual video analysis and display, using the self-similar structure of the color of the human object as the basic feature, it is described by the geometric algebra method, and the human body in the video image is extracted using template convolution objects and design adaptive template functions to extract human movements from sports dance video images. By loading each test video in turn, the posture of the characters in the video is checked every 30 s, and the approximate positions of the head and feet are marked and output to a txt file. Because lying on the ground during sports dance is a way of matching by height correction, the accuracy is less disturbed, but it can still reach 90.9%. The research results show that the template matching robot model proposed in this paper can accurately and robustly extract human objects in video.

Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder.

A new method for the detection of important scenes in baseball videos via a time-lag-aware multimodal variational autoencoder (Tl-MVAE) is presented in this paper. Tl-MVAE estimates latent features calculated from tweet, video, and audio features extracted from tweets and videos. Then, important scenes are detected by estimating the probability of the scene being important from estimated latent features. It should be noted that there exist time-lags between tweets posted by users and videos. To consider the time-lags between tweet features and other features calculated from corresponding multiple previous events, the feature transformation based on feature correlation considering such time-lags is newly introduced to the encoder in MVAE in the proposed method. This is the biggest contribution of the Tl-MVAE. Experimental results obtained from actual baseball videos and their corresponding tweets show the effectiveness of the proposed method.

Research on the Attribute Extraction Algorithm of People and Vehicles Based on Video Structure

With the development of sensor technology and computer vision, the speed of video generation is increasing exponentially. The workload of video browsing and the amount of stored data are increasing. In order to achieve fast browsing and efficient storage, video structure technology is increasingly important. This paper focuses on the video structure technology in the construction of smart cities, outlines the system process of video structure, and studies the extraction algorithm of human and vehicle target attributes based on deep learning. And combined with the actual video monitoring scene, the experiment is carried out. Compared with traditional image processing methods, the video structure target attribute extraction model based on deep learning has high detection accuracy and strong algorithm generalization ability. The intelligent analysis and processing of data by video structure technology is conducive to quickly increasing the speed of target search, reducing storage capacity, solving the problem of long-term storage, and realizing full automation of surveillance video.

Traffic Flow Statistics Method and Practice Based on Roadside Video with YOLO_V3

Temporary traffic flow observation plays a pivotal role in transportation planning, consultation, and decision-making, and it supplements fixed long-term traffic observation data. Although substantial automated technology has been used for traffic observation, such technology is limited by the temporariness and uncertainty of observation points; moreover, these methods are difficult to use in temporary observations. In this study, the YOLO_V3 algorithm, which is based on deep learning, is used for vehicle detection based on roadside videos of temporary observations. Moreover, a secondary detection framework based on vehicle detection and traffic counting regions is proposed. Then, a traffic counting pattern with Kalman filter, Hungarian allocation, and perspective projection transformation is established. In addition, by collecting multiple sets of actual video data, the effectiveness of the method under different conditions is analyzed in terms of three indicators: camera intersection angle with the road, erection height, and road traffic density. Results show that at a camera height of 3 m and a roadside angle of 30°, the accuracy is approximately 95%. However, traffic flow accuracy drops to approximately 90% when vehicles are blocked by large buses and trucks during detection. In this study, the algorithm is tested for execution efficiency using 1080p video streams on a Windows 10 x 64 operating system, a 2080Ti graphics card, a 64 GB RAM, and an i7-7820X CPU. Results show that camera installation angle and height have no considerable effect on operation efficiency. Under low-density traffic, the frames per second (FPS) value is approximately 44; under high-density traffic, the FPS value drops to approximately 33, indicating that the method still has high execution efficiency and can be used for real-time video traffic counting.

A two-stage restoration of distorted underwater images using compressive sensing and image registration

Imaging through a time-varying water surface exhibits severe non-rigid geometric distortions and motion blur. Theoretically, although the water surface possesses smoothness and temporal periodicity, random fluctuations are inevitable in an actual video sequence. Meanwhile, considering the distribution of information, the image structure contributes more to the restoration. In this paper, a new two-stage restoration method for distorted underwater video sequences is presented. During the first stage, salient feature points, which are selected through multiple methods, are tracked across the frames, and the motion fields at all pixels are estimated using a compressive sensing solver to remove the periodic distortions. During the second stage, the combination of a guided filter algorithm and an image registration method is applied to remove the structural-information-oriented residual distortions. Finally, the experiment results show that the method outperforms other state-of-the-art approaches in terms of the recovery effect and time.