Analysis Of Video Data Research Articles

I3D Light - A Simple Motion Information Stream for I3D

Vision-based Human Activity Recognition (HAR) aims to recognize human activities based on the analysis of video data, and has extensive applications in modern industry and human life. Inflated 3D (I3D) is a deep learning architecture that is commonly used for action recognition by using two-stream video data: RGB stream and optical flow stream. I3D achieved great success on a variety of action recognition benchmarks. However, the use of optical flow incurs high computational cost, making the approach unsuitable for real-time applications. We propose an alternative simple motion information extractor to replace the optical flow branch and reduce the computational cost. It is a modified I3D that uses 128 frames of 112x112 images as input. The low spatial resolution and long temporal range of the proposed I3D RGB stream can reduce the spatial information and enhance the motion information. Experiments show that this simple motion stream can increase the accuracy of the original I3D spatial stream by 4.09% on the Kinetics 400 dataset.

Temporal demands of elite fencing.

There is no published study that analyses the temporal demands of fencing, in its six Olympic modalities in the same high-level competition. The only existing publications date back decades, with already obsolete regulations, or focus on a specific modality. The aim of this study is to carry out a chronometric analysis of elite fencing at the 2014 Fencing World Championships to determine the existence of differences between its weapons (épée, foil and sabre) and between genders. A total of 96 elite fencers (48 women) of 24 different nationalities were analysed in the final rounds (direct elimination). We undertook observational methodology based on an open and systematic process with ideographic, point and multi-dimensional design. We analysed the work time, rest time, total time, number of stops and work-to-rest ratio. The Lince software was used to record and analyse video data. The coding and recording process of the 83 fights analysed generated a matrix of 5900 records. The quality of the data was verified by checking the validity of the observation instrument and the intraobserver reliability. The mean work time (between Allez and Halte) was 17.9 ± 3.1 s for épée, 5.8 ± 2.5 s for foil and 1 .7 ± 0.4 s for sabre. The fight work-to-rest ratios recorded were 1:0.9, 1:2.6 and 1:9.2 for épée, foil and sabre respectively. The results showed significant differences (p < .05) in the work and rest times between the three fencing weapons. However, there were no differences between fencers of the same weapon according to gender.

Studying the quality of inquiry-based teaching in science classrooms. A systematic video study of inquiry-based science teaching in primary and lower-secondary schools

ABSTRACT A large body of research has studied the role and potential of inquiry to increase the quality of teaching in science education. While much of this existing research is based on international large-scale assessment studies, we still lack a clear understanding of the factors that influence the quality of inquiry-based science teaching in actual classroom practices. In this paper, we operationalise teaching quality through an observation manual, and we drew on this manual to systematically analyse video data of instructional practices in 20 Norwegian science classrooms at the primary and lower-secondary school level (73 observed lessons and about 450 students). We identified varying quality in the individual inquiry phases and differences between primary and lower-secondary schools. We observed that primary-school students collected and documented data more systematically than lower-secondary students and that consolidations were slightly more emphasised and of higher quality at the lower-secondary than at the primary level. Moreover, our findings indicate that inquiry-based teaching gave students more freedom to make their own choices and increased the quality of student participation in the classroom. Based on our findings, we discuss how teachers can improve the quality of inquiry-based instruction and empower students in the classroom.

Multiview abnormal video synopsis in real-time

The proliferation of surveillance cameras has resulted in an exponential growth in video data, which has created several challenges for video data analysis, extraction, and storage. Video synopsis is an efficient methodology for analyzing and storing video data because it creates short videos. However, existing video synopsis methods are inappropriate for abnormal behavior situations (e.g., constructing a video synopsis of only abnormal frames emphasizing gun and knife characteristics to predict violent activity). As most existing video synopsis methods rely on the foreground and multiple objects tracking preprocessing, they perform poorly in real-time scenarios, particularly in crowded situations. We proposed a video synopsis approach suitable for multiview cameras to mitigate this problem; it can also be applied to radically diverse crowdedness. A considerable difference exists between the proposed method and existing approaches because it has several unique properties. We first detected abnormal segments from a given video and then tracked and extracted the frames. We can extract the required meaningful data by applying this method, which increases the synopsis generation performance. Second, we created a summary of all the videos. Finally, a stitching algorithm was used to create the synopsis.

Investigating the differences in human behavior between conventional machining and CNC machining for future workforce development: A case study

Due to the high efficiency and consistent product quality, CNC machining has gained a dominant role in the modern machining industry. However, conventional machining still has its significance for certain production settings, e.g. prototyping, and machining workforce preparation. CNC and conventional machining require particular skills and knowledge, which can be unique to specific types of machine tools or overlap to a certain extent. With increasing production volume demands and an aging workforce, a need for efficient quantification, preservation and transfer of said skillsets arises to ensure effective preparation of future professionals and the undisrupted operation of the manufacturing industry. Moreover, the observed shift towards human-centered manufacturing systems in the Industry 4.0 necessitates obtaining an in-depth understanding of human roles in machining. The following paper proposes a novel research approach based on collection and analysis of eye-tracking and video data supplemented by verbal interviews, surveys and self-assessment. A conducted case study spans the entirety of the machining process, from part evaluation, cutting strategy determination, machining operations, to process re-evaluation and optimization. The results show that far greater variability in cutting strategy in terms of operation order, number of operations and used production tooling between consecutive production runs can be observed for conventional machining, with little variation in those terms noted for CNC-based production. Overall, the collected data has allowed to gain an insight into the machinist's decision-making processes and the rationale for observed cutting strategy changes, allowing for potential future application of the proposed research method in improving the machining training and potentially aiding process design by applying the outcomes of studies performed with the use of presented research method to expert systems and future CAM/CAE software solutions.

Rotation speed assessment for idlers in belt conveyors using image analysis

Monitoring and diagnostics of elements of the belt conveyor is a challenging task. One of the hardest aspects is dealing with objects such as rolling idlers that support the belt. They are distributed along the entire length of a conveyor, and their amount can easily reach thousands. In the ideal case, every idler should undergo in-situ monitoring, however, this is impractical. Hence, it is proposed to use remote sensing to acquire knowledge about some aspects of their condition. In particular, the authors propose to measure the rotational speed of the idler based on short video recordings. This data could be captured by mobile inspection robots, such as UAVs or UGVs, or even manually. Detecting the fact that the idler rotates with improper speed (lower than nominal), could indicate a developing fault of the internal bearing. An idler that rotates slower than it should also create additional friction between itself and the belt. This friction can increase the overall operational resistances, and even cause fire due to increased temperature. In this paper, the authors present a simple method for establishing the rotational speed of the idler based on video data analysis.

Sign Language Detection in Voice Output

This Sign language detection is the process of recognizing and interpreting gestures, symbols, and movements used in sign language. This technology is significant in enabling communication between individuals who are deaf or hard of hearing and those who do not know sign language. Sign language detection uses video data analysis and identifies specific features such as hand and arm movements, facial expressions, and body language. Machine learning techniques, specifically convolutional neural networks, are used to recognize and classify specific signs and gestures with an accuracy rate of 80-90%. To enhance the system's usability, the policy should include record and history tabs. Additionally, the dataset can be modified to suit different sign languages. Overall, this technology offers immense potential for education and communication for individuals with hearing impairments

Problems on the pavement?: A study of E-scooterists amongst co-movers

ABSTRACT There is now a substantial research literature on e-scootering, mostly based on survey, interview and statistical-type data. This is useful, however, the detail is lacking on exactly how e-scooterists get through public space amongst co-movers. Because e-scooterists often intrude in a space where pedestrians used to have priority, it is often assumed there will be “problems on the pavement”, partly because e-scooterists seem to have advantages of speed and visible presence. But in studying this topic we need to move beyond assumptions to careful examination of naturalistic data. Consequently, this study offers a qualitative analysis of video data collected by discreetly following e-scooterists and filming their actions amongst co-movers in specific places. Through the presentation of graphic transcripts and a comparative approach, new knowledge is gained on the complexities of e-scooter use within the public space of the city.

TRAFFIC ANALYSIS USING IOT

Traffic congestion is a major problem in many cities of India along with other countries. Failure of signals, poor law enforcement and bad traffic management has lead to traffic congestion. One of the major problems with Indian cities is that the existing infrastructure cannot be expanded more, and thus the only option available is better management of the traffic. Traffic congestion has a negative impact on economy, the environment and the overall quality of life. Hence it is high time to effectively manage the traffic congestion problem. There are various methods available for traffic management such as video data analysis, infrared sensors, inductive loop detection, wireless sensor network, etc. All these methods are effective methods of smart traffic management. But the problem with these systems is that the installation time, the cost incurred for the installation and maintenance of the system is very high. Hence a new technology called Smart Traffic Analysis System which helps to detect strength of traffic on each side and release the traffic based on strength so that it will reduce congestion problem.

Modern automatic recognition technologies for visual communication tools

Communication refers to a wide range of different behaviors and activities aimed at handing over information. The communication process includes verbal, paraverbal and non-verbal components, conveying the informational part of a message and its emotional part respectively. A complex analysis of all communication components makes it possible to evaluate not only the content, but also the situational context of what is being said, as well as to identify additional factors inherent in the mental and somatic state of the speaker. There are several methods of conveying a verbal message, among which are oral and gestural speech (such as the sign language and fingerspelling). Various forms of communication can be contained in multiple data transmission channels, such as audio or video channels. The review is concerned with video data analysis systems, as the audio channel is incapable of non-verbal components transmission that contribute supplemental details. The article analyzes databases of static and dynamic images and systems, developed to recognize the verbal component conveyed by oral and gestural speech, as well as systems that evaluate paraverbal and non-verbal components of communication. Challenges of designing such databases and systems are specified. Prospective directions in complex analysis of all communication components and its combinations for the most complete evaluation of messages are defined.

Smart Traffic Light: Diwaniyah City District Center As A Case Study

Traffic congestion is a significant problem in many cities in India and other countries. Failure of signals, poor law enforcement, and bad traffic management have led to traffic congestion. One of the major problems with Indian cities is that the existing infrastructure cannot be expanded more, and thus, the only option available is better traffic management. Traffic congestion hurts the economy, the environment, and the overall quality of life. Hence, it is high time we effectively managed the traffic congestion problem. There are various methods available for traffic management, such as video data analysis, infrared sensors, inductive loop detection, wireless sensor networks, etc. All these methods are effective methods of intelligent traffic management. However, the problem with these systems is that the installation time and the cost incurred for installing and maintaining the system are very high. Hence, a new technology called Radio Frequency Identification (RFID) is introduced, which can be coupled with the existing signaling system and act as a key to intelligent traffic management in real-time. This new technology will require less time for installation and will have fewer costs compared to other methods of traffic congestion management. The use of this new technology will lead to reduced traffic congestion. Bottlenecks will be detected early, so early preventive measures can be taken, thus saving the driver time and money.

The method of the real-time human detection and tracking

Today, data collected from video surveillance systems require processing. Video content analysis (VCA) or video analytics (VA) has found applications in security systems, retail, the automotive industry, smart home technologies, etc. The tasks of video analytics include the detection and tracking of objects. Usually, video analytics systems are specialized software for analyzing video data collected from webcams and intelligently assessing the situation. There are also separate video surveillance cameras with built-in video analytics functions. Software approaches to human detection and tracking are quite diverse, special applications and web applications or separate software modules are created. This work presents an approach to solving these problems using convolutional neural networks. The aim of the study is to increase the efficiency of human detection and tracking in video sequences. For this purpose, an overview of existing methods of detecting objects in images was conducted, in particular: the Viola-Jones algorithm, the histogram of oriented gradients. The choice of convolutional neural networks for solving the subtask of object detection is substantiated. The working principles, advantages and disadvantages of Faster R-CNN, YOLO, SSD and RetinaNet networks are considered. Their comparative analysis was carried out according to the indicators of speed and accuracy of recognition on the HABBOF dataset. A hybrid neural network for human detection and tracking has been developed: a convolutional neural network of the YOLO type has been improved. The created network achieved an accuracy of 39.2% at 43 frames per second. Experiments were carried out with the created network in order to evaluate the operation of the network in various conditions. It has been established that it works well in poor lighting conditions, but the issue of detecting small objects remains open.

A Survey on Compression Domain Image and Video Data Processing and Analysis Techniques

A tremendous amount of image and video data are being generated and shared in our daily lives. Image and video data are typically stored and transmitted in compressed form in order to reduce storage space and transmission time. The processing and analysis of compressed image and video data can greatly reduce input data size and eliminate the need for decompression and recompression, thereby achieving significant savings in memory and computation time. There exists a body of research on compression domain data processing and analysis. This survey focuses on the work related to image and video data. The papers cited are categorized based on their target applications, including image and video resizing and retrieval, information hiding and watermark embedding, image and video enhancement and segmentation, object and motion detection, as well as pattern classification, among several other applications. Key methods used for these applications are explained and discussed. Comparisons are drawn among similar approaches. We then point out possible directions of further research.

Visualization and Visual Analytics Approaches for Image and Video Datasets: A Survey

Image and video data analysis has become an increasingly important research area with applications in different domains such as security surveillance, healthcare, augmented and virtual reality, video and image editing, activity analysis and recognition, synthetic content generation, distance education, telepresence, remote sensing, sports analytics, art, non-photorealistic rendering, search engines, and social media. Recent advances in Artificial Intelligence (AI) and particularly deep learning have sparked new research challenges and led to significant advancements, especially in image and video analysis. These advancements have also resulted in significant research and development in other areas such as visualization and visual analytics, and have created new opportunities for future lines of research. In this survey article, we present the current state of the art at the intersection of visualization and visual analytics, and image and video data analysis. We categorize the visualization articles included in our survey based on different taxonomies used in visualization and visual analytics research. We review these articles in terms of task requirements, tools, datasets, and application areas. We also discuss insights based on our survey results, trends and patterns, the current focus of visualization research, and opportunities for future research.

Development of a chub mackerel with less-aggressive fry stage by genome editing of arginine vasotocin receptor V1a2

Genome editing is a technology that can remarkably accelerate crop and animal breeding via artificial induction of desired traits with high accuracy. This study aimed to develop a chub mackerel variety with reduced aggression using an experimental system that enables efficient egg collection and genome editing. Sexual maturation and control of spawning season and time were technologically facilitated by controlling the photoperiod and water temperature of the rearing tank. In addition, appropriate low-temperature treatment conditions for delaying cleavage, shape of the glass capillary, and injection site were examined in detail in order to develop an efficient and robust microinjection system for the study. An arginine vasotocin receptor V1a2 (V1a2) knockout (KO) strain of chub mackerel was developed in order to reduce the frequency of cannibalistic behavior at the fry stage. Video data analysis using bioimage informatics quantified the frequency of aggressive behavior, indicating a significant 46% reduction (P = 0.0229) in the frequency of cannibalistic behavior than in wild type. Furthermore, in the V1a2 KO strain, the frequency of collisions with the wall and oxygen consumption also decreased. Overall, the manageable and calm phenotype reported here can potentially contribute to the development of a stable and sustainable marine product.

Video Data Analysis and Police Body-Worn Camera Footage

Video data analysis (VDA) represents an important methodological framework for contemporary research approaches to the myriad of footage available from cameras, devices, and phones. Footage from police body-worn cameras (BWCs) is anticipated to be a widely available platform for social science researchers to scrutinize the interactions between police and citizens. We examine issues of validity and reliability as related to BWCs in the context of VDA, based on an assessment of the quality of audio and video obtained from that platform. Second, we compare the coding of BWC footage obtained from a sample of police-citizen encounters to coding of the same events by on-scene coders using an instrument adapted from in-person systematic social observations (SSOs). Findings show that there are substantial and systematic audio and video gaps present in BWC footage as a source of data for social science investigation that likely impact the reliability of measures. Despite these problems, BWC data have substantial capacity for judging sequential developments, causal ordering, and the duration of events. Thus, the technology should open theoretical frames that are too cumbersome for in-person observation. Theoretical development with VDA in mind is suggested as an important pathway for future researchers in terms of framing data collection from BWCs and also suggesting areas where triangulation is essential.

Screenshotting What’s Important in Video Data: An Experiment in Collaborative, Subjective Analysis of Artifactual, Cultural Research with Children

When using video and visual methods in qualitative and post-qualitative research, the size and scale of the data set can be overwhelming, particularly for new researchers. Collaborative research teams often work with a code book to systematize and unify their analyses. Interpretive researchers pursuing multi-layered and multi-voiced visual analysis often find it difficult to move away from desires for a single ‘best’ interpretation of what happened. This paper illustrates and interrogates an open and flexible method for ‘thinning’ (screenshotting) video data that we call the ‘Five Images Method’. We offer one unfolding of interpretive processes and tensions and examine how four researchers worked across positionalities to analyse video data. We start with our positionalities in relation to a research study of children creating photographic and written stories of cultural artifacts, carried out over one year. The primary data from the study was generated through online video-conference sessions connecting a university researcher with an elementary class. A second level of data was created through a process of screenshotting, followed by recursive cycles of conversation about the choices of each researcher, and how they were guided by background, geography, roles in relation to child participants, technologies, personal experiences, and so on. Two key incidents that illustrate the potential of the method and the interpretations produced are described. We argue that reducing video data in this way can be both generative and limiting, while also serving as a catalyst for enhanced analysis. The collaborations and relationships built in research teams through slow processes of analysis (and writing!) working across difference also promote evocative and layered learning. Looking at interpretations as multiple can be hampered by longstanding histories of research as intended to produce authentic and singular truths.

Observations on strategies used by people with dementia to manage being assessed using validated measures: A pilot qualitative video analysis.

Analysis of video data was conducted of validated assessments with people with dementia as part of a feasibility control study comparing a lifelong learning service with other dementia services. The aim was to provide a new perspective on what occurs during the assessment process when using validated measures in research and explore which strategies people with dementia use to manage their participation. Video recordings were made of pre- and postintervention assessments of people with dementia. An initial pilot analysis of 10 videos of the pre-assessments was conducted. Lifelong learning services and other dementia services situated in six municipalities in Northern Denmark took part in this study, with 55 people with dementia participating. The themes identified were: 'State of mind'and 'Mental resources', showing how these aspects influenced the participants' reactions and the strategies they used. The results are discussed in relation to how individual personality traits influence the assessment process and the way a person with dementia will manage the situation. The assessment situation is complex and can be influenced by the strategies adopted by individuals with dementia as they try to manage the assessment process. People with dementia supported the decision-making for the choice of validated measure used within this study.

A Review on Traffic Management and Road Analysis of Porwal Road

Abstract: Traffic congestion is a major problem in many cities of India along with other countries. Failure of signals, poor law enforcement, and bad traffic management has to lead to traffic congestion. One of the major problems with Indian cities is that the existing infrastructure cannot be expanded more, and thus the only option available is better management of the traffic. Traffic congestion has a negative impact on the economy, the environment, and the overall quality of life. Hence it is high time to effectively manage the traffic congestion problem. In cities, where the number of vehicles continuously increases faster than the available traffic infrastructure to support them, congestion is a difficult issue to deal with and it becomes even worse in case of car accidents. There are various methods available for traffic management such as video data analysis, infrared sensors, inductive loop detection, wireless sensor network, etc. Traffic congestion is a major problem in many cities of India along with other countries. Failure of signals, poor law enforcement, and bad traffic management have led to traffic congestion. This problem affects many aspects of modern society, including economic development, traffic accidents, increase in greenhouse emissions, time spent, and health damages.

Learning to innovate: Students and teachers constructing collective innovation practices in a primary school’s makerspace

The need to foster citizens’ innovation skills is widely recognized. Although current research acknowledges the potential of makerspaces to promote innovation activities, research still lacks an understanding of underlying mechanisms that can lead the creation of innovations in makerspaces by students. Moreover, research to date has overlooked how innovation practices are formed in K–12 makerspaces. In this sociocultural study, we used ethnographic video data from a Finnish primary school’s makerspace and applied methods of abductive Video Data Analysis to investigate how innovation practices are constructed in first to sixth grade students’ and teachers’ interactions. The results of this study show that the innovations created by the students in the makerspace were an outcome of students’ and teachers’ collective innovation practices. The study provides a typology of these collective innovation practices, namely: taking joint action to innovate, navigating a network of resources, and sustaining innovation activities. Further, our results reveal that the collective actions encouraged students to use skills deemed to be important for innovation creation. Also, adding to existing research knowledge, our results reveal two mechanisms that potentially promote students’ learning to innovate. These mechanisms include the teachers’ orientation to facilitating open-ended STEAM projects and practices that emphasize students’ ownership over their personal projects.