Open Video Research Articles

An effective Key Frame Extraction technique based on Feature Fusion and Fuzzy-C means clustering with Artificial Hummingbird

Key frame extraction is very important in video summarization and content-based video analysis to address the problem of data redundancy in a video. Key frame extraction enables quick navigation and expert video arrangement in many applications. The visually impaired can benefit from the use of key frame extraction for rapid object recognition and tracking. Most key frame extraction techniques consider only a single visual feature instead of multiple features or full pictorial information of the video. This study proposes a key frame extraction method from a video that (i) first removes insignificant frames by pre-processing, (ii) second, four visual and structural feature differences among the consecutive frames are extracted and aggregated to identify informative frames, (iii) third, to cluster the obtained frames, a hybrid FCM-AHA method is proposed by combining Fuzzy C-means(FCM) with artificial hummingbird optimization algorithm (AHA) to circumvent the local minima trapping problem of FCM, and finally, from each cluster, the two frames having greatest Euclidean distance from all the other frames within a cluster is selected as key frames to remove redundant frames. Experimental results on the Open video and YouTube datasets show that the suggested method outperforms state-of-the-art methods both in terms of subjective qualitative analysis and objective quantitative evaluation, e.g., Precision, Recall, and F-score. Further, results are also taken on real video to demonstrate its applicability in real-life applications.

Devising an analytical method for estimating aircraft positioning accuracy by an infocommunication network of optoelectronic stations

The object of this study is the monitoring processes of airspace in the visible and infrared frequency ranges using an infocommunication network of optoelectronic tracking stations. The problem addressed is the assessment of accuracy in positioning aerial vehicles in airspace, considering random and systematic errors in video surveillance. The proposed method allows for the analytical evaluation of variance in the coordinates of aerial vehicles in airspace at any given moment, depending on the variance in errors from all components of the video surveillance process. The following results are reported: – mathematical models for both open and covert video surveillance by the infocommunication network of optoelectronic stations based on the trajectories of aerial vehicles, which enable the estimation of their coordinates for each moment of video surveillance. – analytical relationships between the variance of geolocation of aerial vehicles and the variance of errors in all components of the video surveillance process. A key feature of the method is its practical independence on the type and size of aerial vehicles. The method requires the availability of metrological characteristics of the optoelectronic station instruments and synchronized measurements of azimuth, elevation angle (for covert), and slant range (for open video surveillance). The numerical values of the root mean square deviations of the positioning errors of aerial vehicles under various video surveillance conditions range from 0.1 to 0.35 meters, confirming the effectiveness of using the infocommunication network of optoelectronic stations as an instrumental tool for high-precision trajectory measurements.

Video Hashing with Tensor Robust PCA and Histogram of Optical Flow for Copy Detection

Abstract This paper proposes a novel video hashing with tensor robust Principal Component Analysis (PCA) and Histogram of Optical Flow (HOF) for copy detection. In the proposed hashing, a video is divided into some video groups. For each video group, a low-rank secondary frame is constructed from the low-rank component decomposed by applying tensor robust PCA to the video group. Since the low-rank component can well indicate spatial-temporal intrinsic structure of the video group and it is slightly disturbed by digital operations, feature extraction from the low-rank secondary frames is discriminative and stable. Next, spatial features and temporal features are extracted from low-rank secondary frames by Charlier moments and HOF, respectively. Since the Charlier moments are robust to geometric transform and they can efficiently distinguish video frames with different contents, the use of Charlier moments can make robust and discriminative spatial features. As the HOF can measure the distribution of motion information between frames, the temporal features formed by HOFs can provide good discrimination. Hash is ultimately determined by quantizing the spatial and temporal features and concatenating the quantized results. Numerous experiments on open video datasets indicate that the proposed hashing is superior to some hashing baseline schemes in terms of classification and copy detection.

Cholec80-CVS: An open dataset with an evaluation of Strasberg’s critical view of safety for AI

Strasberg’s criteria to detect a critical view of safety is a widely known strategy to reduce bile duct injuries during laparoscopic cholecystectomy. In spite of its popularity and efficiency, recent studies have shown that human miss-identification errors have led to important bile duct injuries occurrence rates. Developing tools based on artificial intelligence that facilitate the identification of a critical view of safety in cholecystectomy surgeries can potentially minimize the risk of such injuries. With this goal in mind, we present Cholec80-CVS, the first open dataset with video annotations of Strasberg’s Critical View of Safety (CVS) criteria. Our dataset contains CVS criteria annotations provided by skilled surgeons for all videos in the well-known Cholec80 open video dataset. We consider that Cholec80-CVS is the first step towards the creation of intelligent systems that can assist humans during laparoscopic cholecystectomy.

ViSTORY: Effective Video Storyboard Generation with Visual Keyframes using Discrete Cosine Transform

Nowadays, multimedia content utility is increasing rapidly. Multimedia search engines like Google, Yahoo, Bing, etc., are available just a click away to all users. There are around 500-600 hours of video uploads per unit of time to the Internet. So, among other types of multimedia content, such as text and images, video is the most complicated content for indexing, browsing, and retrieval. Videos give more scope for implementation because of their complex and unstructured nature. This paper proposes a new method of video storyboard generation with keyframe extraction in spatial and frequency domains using Discrete Cosine Transform (DCT) for video summarization. It discusses the empirical appraisal of video visual keyframes with t-test analysis in comparison with spatial and frequency domains, resulting in a quick response to customer demands by providing static storyboards. This study proposes a new performance measure as matching frames by analyzing input videos and the standard benchmarks video dataset, i.e., Open Video Project (OVP) and SumMe. Among all the keyframe extraction techniques, DCT gives higher accuracy and a better matching rate.

Open video data sharing in developmental science and clinical practice

In behavioral research and clinical practice video data has rarely been shared or pooled across sites due to ethical concerns of confidentiality, although the need of shared large-scaled datasets remains increasing. This demand is even more imperative when data-heavy computer-based approaches are involved. To share data while abiding by privacy protection rules, a critical question arises whether efforts at data de-identification reduce data utility? We addressed this question by showcasing an established and video-based diagnostic tool for detecting neurological deficits. We demonstrated for the first time that, for analyzing infant neuromotor functions, pseudonymization by face-blurring video recordings is a viable approach. The redaction did not affect classification accuracy for either human assessors or artificial intelligence methods, suggesting an adequate and easy-to-apply solution for sharing behavioral video data. Our work shall encourage more innovative solutions to share and merge stand-alone video datasets into large data pools to advance science and public health.

Do I Need to Ask Permission? Applying Fair Use to Educational Activities.

Do I Need to Ask Permission? Applying Fair Use to Educational Activities.

Creating Video Visual Storyboard with Static Video Summarization using Fractional Energy of Orthogonal Transforms

The overwhelming number of video uploads and downloads has made it incredibly difficult to find, gather, and archive videos. A static video summarization technique highlights an original video's significant points through a set of static keyframes as a video visual storyboard. The video visual storyboards are created as static video summaries that solve video processing-related issues like storage and retrieval. In this paper, a strategy for effectively summarizing static videos using the feature vectors, which are fractional coefficients of the transformed video frames, is proposed and evaluated. Four popular orthogonal transforms are deployed for generating feature vectors of video frames. The fractional coefficients of transformed video frames taken as 25 percent, 6.25 percent, and 1.5625 percent of full 100 percent transformed coefficients are considered to form video visual storyboards. The proposed method uses the benchmark video datasets Open Video Project (OVP) and SumMe to validate the performance, containing user summaries (storyboards). These video summaries created using the proposed method are evaluated using percentage accuracy and matching rate.

Open Video Data Sharing in Developmental and Behavioural Science

Open Video Data Sharing in Developmental and Behavioural Science

Satisfaction with and Feasibility of Prenatal Counseling via Telemedicine: A Prospective Cohort Study.

Introduction: Telemedicine has become increasingly important over the past decade. With the pressure of the COVID-19 pandemic, demands for remote health care solutions have seen an unprecedented rise. However, many questions regarding the feasibility and benefits of telemedicine remain. The aim of our study was to evaluate both the technical feasibility and patient satisfaction with video consultations in a tertiary center for obstetric care. Materials and Methods: This prospective single-center trial was part of the larger open Video Service project on telemedicine at the Department of Gynecology and Obstetrics at the University of Münster. Patients requiring prenatal or prepregnancy counseling were included. A questionnaire was designed for the evaluation of patient satisfaction and filled in by both patient and health care provider at the end of the video consultation. Results: Of 80 eligible cases, 75 video consultations were carried out and data from the questionnaire were collected. Overall patient satisfaction was high (95%, 71/75) although technical problems occurred in 37% (29/75) of the appointments. Health care providers' satisfaction was equally high and in 88% (66/75) of cases, remote consultations avoided an in-house visit without effect on health care quality. Conclusions: Remote consultations are feasible and yield high satisfaction rates even in a medical field as sensitive as perinatal medicine. Further research is necessary to determine the cost-effectiveness and effects on perinatal outcome. Health care systems should be offered clear guidance on medicolegal issues and funding of remote consultations to integrate telemedicine into routine health care.

Evaluation and collaboration in creating online audio descriptions of visual art

AccesArte is an accessibility project that is part of the internship programme at Kaleidoscope, a non-profit organization founded by the authors to apply audio description (AD) research to developing accessibility programmes. The project consists of ADs of a thematic selection of visual artworks, and the development of eclectic and experience-oriented AD types. It uses online, open access videos and performs a formative and summative evaluation of the resources, with the latter being online, open and ongoing. The goal of this article is to offer a detailed description and critical analysis of the project, with an emphasis on the formative evaluation process. In this regard, the formative evaluation has allowed us to include blind and partially sighted (BPS) consultants in the process, improve resource quality, and enrich the interns’ learning experience.

Making open educational resource videos on sustainable development: students’ attitudes, rationales, and approaches

ABSTRACT Aim and background The aim of this study was to investigate students’ attitudes, rationales, and approaches to making open educational resource (OER) videos (a form of OERs) on sustainable development (SD) in order to identify students’ competencies and effective pedagogical designs. Method Students registering for a teacher training course were invited to design and create pedagogies, make OER videos, and share the videos on YouTube on five SD topics: sustainable lifestyle, campus, community, enterprise, and earth development. The students provided their weekly journals and a final reflection on the whole process of making the OER videos on SD. This study used qualitative data analysis and text mining methodologies to analyse students’ process data of making OER videos on SD. Results and discussion The analysis results revealed that making OER videos on SD needed students’ ideational, inquiry, societal, and disciplinary competencies. Inferred pedagogical suggestions for practitioners to support students in making digital products on SD are to follow a linear pathway from ideational creation, inquiry process, societal transformation, to transdisciplinary reflection.

Research on College English Teaching Path under the Background of “Internet+”

With the advent of the Internet era, various technologies have been continuously developed and applied in practice, and people’s lives have undergone earth-shaking changes. Under such a development trend, how to achieve the integration of Internet technology and college English teaching? The effective connection of various resources has become the focus of more and more colleges and universities in the reform of English teaching at this stage. With the technical support of the Internet, various online classes such as open video classes, MOOCs, and flipped classrooms have achieved great success. The development of these classes has not only explored a new direction for college English teaching reform, but also college English. The teaching reform has played a leading role. This article will explore how to better carry out innovative reforms in college English classrooms under the background of “Internet +”, and realize the innovation of teaching methods, so as to cultivate more English professionals.

A Video Production Method of Microclass Combined with MOOC

In recent years, video production has become one of the key challenges for researchers in the area. In the face of the poor video effects of earlier video production methods and approaches, this study has considered the MOOC-based microclass combined with the MOOC video production method for improving the video production effect. According to the idea of MOOC, based on the early design stage and shooting preparation stage of video development, the video development team prepares the text, manuscript, and course type according to the initial design stage of video development and formulates the recording process. In the video recording stage, all activities of classroom teaching are recorded through the multislot mode for enhancing the sense of scene of the video picture. In the later stage of video production, video editing and feature processing are implemented by means of film and television to improve the effect of video production. The video production method based on the Hidden Markov model is used to ensure the continuity of front and back shots and complete online open video production and release to the cloud. The experimental results of the study show that the application effect of this method is effective, and the average satisfaction is as high and measured as 91.36%.

Machine learning-based energy consumption modeling and comparison of H.264/AVC and google VP8 encoders

Advancement of the prediction models used in a variety of fields is a result of the contribution of machine learning approaches. Utilizing such modeling in feature engineering is exceptionally imperative and required. In this research, we show how to utilize machine learning to save time in research experiments, where we save more than five thousand hours of measuring the energy consumption of encoding recordings. Since measuring the energy consumption has got to be done by humans and since we require more than eleven thousand experiments to cover all the combinations of video sequences, video bit_rate, and video encoding settings, we utilize machine learning to model the energy consumption utilizing linear regression. VP8 codec has been offered by Google as an open video encoder in an effort to replace the popular MPEG-4 Part 10, known as H.264/AVC video encoder standard. This research model energy consumption and describes the major differences between H.264/AVC and VP8 encoders in terms of energy consumption and performance through experiments that are based on machine learning modeling. Twenty-nine raw video sequences are used, offering a wide range of resolutions and contents, with the frame sizes ranging from QCIF(176x144) to 2160p(3840x2160). For fairness in comparison analysis, we use seven settings in VP8 encoder and fifteen types of tuning in H.264/AVC. The settings cover various video qualities. The performance metrics include video qualities, encoding time, and encoding energy consumption.

Keyframe Generation Method via Improved Clustering and Silhouette Coefficient for Video Summarization

In order to solve the issue that the traditional k-means algorithm falls into the local optimal solution in video summarization due to unreasonable initial parameter setting, a video summarization generation algorithm by using improved clustering and silhouette coefficient was proposed. Firstly, color features and texture features are extracted and fused from the decomposed video frames. Secondly, the hierarchical clustering algorithm is used to obtain the initial clustering results. And then, the improved k-means algorithm with silhouette coefficient is introduced to optimize the initial clustering results. Finally, the nearest frame from the cluster center is selected as the keyframe, and all the final keyframes are arranged in the order of the time sequence in the original video to constitute video summarization. The proposed algorithm is evaluated on the benchmark Open Video Database dataset with an average 71% precision, 84% recall rate, and 76% F-score, which is higher than state-of-the-art video summarization methods. Moreover, it generates video keyframes that are closer to user summaries, and it improves effectively the overall quality of the generated summary.

Open Video Game Development and Participatory Design

ABSTRACT This article analyzes user work during open game development and presents an alternative model for participatory design. During open development, developers publicly distribute incomplete games, discuss their design goals, and facilitate user feedback. This article examines user work on an open development forum using conventional content and discourse uptake analyses. It finds that users customize their participation, engage with multiple objects of design, and affect design through collective action.

Compact video fingerprinting via an improved capsule net

Robustness, distinctiveness and compactness are the three basic performance metrics for video fingerprinting, and the three factors affect each other. It is challenging to improve them simultaneously. For this reason, an end-to-end fingerprinting via a capsule net is proposed. In order to capture video features, a capsule net, based on a 3D/2D mixed convolution module, is designed, which maps raw data to compact real vector directly. A new designed adaptive margin triplet loss function is introduced, and it can automatically adjust the loss according to the sample distance. It is beneficial for reducing training difficulty and improving performance. Three open access video datasets FCVID, TRECVID and You Tube are composed to train and test, large experimental results have shown that the proposed fingerprinting achieves better performance than traditional and deep learning methods.

Opening the Black Box of Team Performance With Open-Source Games: A Review and Recommendations

Many important indicators of the inputs, mediators and outcomes of team performance have remained stubbornly resistant to sustained and detailed measurement. Team-based video games, now played by untold millions of people worldwide, offer the potential to provide data to support the analysis of these and other team-centered phenomena; however, theoretically grounded guidance is needed to guide game selection and modification to support such analysis. Accordingly, this paper explores prospects and challenges for using currently available open video games (that is, ones which are free, open-source, and open-architecture) to improve understanding of team performance. The approach combines systematic classification, structured critique and close analysis to identify games with strong potential for explorations of teamwork by human or mixed human-agent teams. The paper concludes with a discussion of promising trajectories for future research.

BSQ-rate: a New Approach for Video-codec Performance Comparison and Drawbacks of Current Solutions

This paper is dedicated to the analysis of the existing approaches to video codecs comparisons. It includes the revealed drawbacks of popular comparison methods and proposes new techniques. The performed analysis of user-generated videos collection showed that two of the most popular open video collections from media.xiph.org which are widely used for video-codecs analysis and development do not cover real-life videos complexity distribution. A method for creating representative video sets covering all segments of user videos the spatial and temporal complexity is also proposed. One of the sections discusses video quality estimation algorithms used for video codec comparisons and shows the disadvantages of popular methods VMAF and NIQE. Also, the paper describes the drawbacks of the BD-rate – generally used method for video codecs final ranking during comparisons. A new ranking method called BSQ-rate which considers the identified issues is proposed. The results of this investigation were obtained during the series of research conducted as part of the annual video-codecs comparisons organized by video group of computer graphics and multimedia laboratory at Moscow State University.