Video Lecture Capture : Student Engagement

Video Capture of university lectures enables learners to be more flexible in their learning behaviour, for instance choosing to attend lectures in person or watch later. However attendance at lectures has been linked to academic success and is of concern for faculty staff contemplating the introduction of Video Lecture Capture. This research study was devised to assess the impact on learning of recording lectures in computer programming courses. The study also considered behavioural trends and attitudes of the students watching recorded lectures, such as when, where, frequency, duration and viewing devices used. The findings suggest there is no detrimental effect on attendance at lectures with video materials being used to support continual and reinforced learning with most access occurring at assessment periods. The analysis of the viewing behaviours provides a rich and accessible data source that could be potentially leveraged to improve lecture quality and enhance lecturer and learning performance.

Partial Encryption by Randomized Zig-Zag Scanning for Video Encoding

19.Wang, Y, O’Neill, M, Kurugollu, F, Partial Encryption by Randomized Zig-Zag Scanning for Video Encoding, IEEE International Symposium on Circuits and Systems (ISCAS), Beijing, May 2013

Evidential event inference in transport video surveillance

This paper presents a new framework for multi-subject event inference in surveillance video, where measurements produced by low-level vision analytics usually are noisy, incomplete or incorrect. Our goal is to infer the composite events undertaken by each subject from noise observations. To achieve this, we consider the temporal characteristics of event relations and propose a method to correctly associate the detected events with individual subjects. The Dempster–Shafer (DS) theory of belief functions is used to infer events of interest from the results of our vision analytics and to measure conflicts occurring during the event association. Our system is evaluated against a number of videos that present passenger behaviours on a public transport platform namely buses at different levels of complexity. The experimental results demonstrate that by reasoning with spatio-temporal correlations, the proposed method achieves a satisfying performance when associating atomic events and recognising composite events involving multiple subjects in dynamic environments.

A Steganalysis System Utilizing Temporal Pixel Correlation Of HEVC Video

High Efficiency Video Coding (HEVC) is the most recent video codec coming after currently most popular H.264/MPEG4 codecs and has promising compression capabilities. It is conjectured that it will be a substitute for current video compression standards. However, to the best knowledge of the authors, none of the current video steganalysis methods designed or tested with HEVC video. In this paper, pixel domain steganography applied on HEVC video is targeted for the first time. Also, its the first paper that employs accordion unfolding transformation, which merges temporal and spatial correlation, in pixel domain video steganalysis. With help of the transformation, temporal correlation is incorporated into the system. Its demonstrated for three different feature sets that integrating temporal dependency substantially increased the detection accuracy.

Neurology assessment by objective structured video examination (OSVE)

Summary
Background
The ability to carry out a neurological examination and make an appropriate differential diagnosis is one of the mainstays of our final Bachelor of Medicine (MB) exam; however, with the introduction of objective structured clinical examinations (OSCEs) it has become impossible to arrange for adequate numbers of suitable real patients to participate in the exam.

Context
It is vital that newly qualified doctors can perform a basic neurological examination, interpret the physical signs and formulate a differential diagnosis.

It is vital that newly qualified doctors can perform a basic neurological examination

Innovation
Since 2010 we have introduced an objective structured video examination (OSVE) of a neurological examination of a real patient as part of our final MB OSCE exam. The students view clips of parts of the examination process. They answer questions on the signs that are demonstrated and formulate a differential diagnosis.

Implications
This type of station is logistically a lot easier to organise than a large number of real patients at different examination sites. The featured patients have clearly demonstrated signs and, as every student sees the same patient, are perfectly standardised. It is highly acceptable to examiners and performed well as an assessment tool. There are, however, certain drawbacks in that we are not examining the student's examination technique or their interaction with the patient. Also, certain signs, in particular the assessment of muscle tone and power, are more difficult for a student to estimate in this situation

Recurrent Convolutional Network for Video-based Person Re-Identification

In this paper we propose a novel recurrent neural networkarchitecture for video-based person re-identification.Given the video sequence of a person, features are extracted from each frame using a convolutional neural network that incorporates a recurrent final layer, which allows information to flow between time-steps. The features from all time steps are then combined using temporal pooling to give an overall appearance feature for the complete sequence. The convolutional network, recurrent layer, and temporal pooling layer, are jointly trained to act as a feature extractor for video-based re-identification using a Siamese network architecture.Our approach makes use of colour and optical flow information in order to capture appearance and motion information which is useful for video re-identification. Experiments are conduced on the iLIDS-VID and PRID-2011 datasets to show that this approach outperforms existing methods of video-based re-identification.

https://github.com/niallmcl/Recurrent-Convolutional-Video-ReID
Project Source Code

Spatio-temporal Rich Model Based Video Steganalysis on Cross Sections of Motion Vector Planes

A rich model based motion vector steganalysis benefiting from both temporal and spatial correlations of motion vectors is proposed in this work. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this work. Firstly, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring motion vectors for longer distances. Therefore, temporal motion vector dependency along side the spatial dependency is utilized for rigorous motion vector steganalysis. Secondly, unlike the filters previously used, which were heuristically designed against a specific motion vector steganography, a diverse set of many filters which can capture aberrations introduced by various motion vector steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent motion vector steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in motion vector steganalysis field including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.

The player and video game interplay in the gameplay experience construct

Among the many discussions and studies related to video games, one of the most recurrent, widely debated and important relates to the experience of playing video games. The gameplay experience – as appropriated in this study – is the result of the interplay between two essential elements: a video game and a player. Existing studies have explored the resulting experience of video game playing from the perspective of the video game or the player, but none appear to equally balance both of these elements. The study presented here contributes to the ongoing debate with a gameplay experience model. The proposed model, which looks to equally balance the video game and the player elements, considers the gameplay experience to be both an interactive experience (related to the process of playing the video game) and an emotional experience (related to the outcome of playing the video game). The mutual influence of these two experiences during video game play ultimately defines the gameplay experience. To this gameplay experience contributes several dimensions, related to both the video game and player: the video game includes a mechanics, interface and narrative dimension; the player includes a motivations, expectations and background dimension. Also, the gameplay experience is initially defined by a gameplay situation, conditioned by an ambient in which gameplay takes place and a platform on which the video game is played. In order to initially validate the proposed model and attempt to show a relationship among the multiple model dimensions, a multi-case study was carried out using two different video games and player samples. In one study, results show significant correlations between multiple model dimensions, and evidence that video game related changes influence player motivations as well as player visual behavior. In specific player related analysis, results show that while players may be different in terms of background and expectations regarding the game, their motivation to play are not necessarily different, even if their performance in the game is weak. While further validation is necessary, this model not only contributes to the gameplay experience debate, but also demonstrates in a given context how player and video game dimensions evolve during video game play.

An Empirical Evaluation of the Reversal Theory State Measure: Tracking Changes in the Experience of Video Commercials of Three Leading Running Brands

Abstract of paper delivered at the 17th International Reversal Theory Conference, Day 3, session 4, 01.07.15

An Empirical Evaluation of the Reversal Theory State Measure Using Three Running Brand Video Commercials

This paper reports on the first known empirical use of the Reversal Theory State Measure (RTSM) since its publication by Desselles et al. (2014). The RTSM was employed to track responses to three purposely-selected video commercials in a between-subjects design. Results of the study provide empirical support for the central conceptual premise of reversal theory, the experience of metamotivational reversals and the ability of the RTSM to capture them. The RTSM was also found to be psychometrically sound after adjustments were made to two of its three component subscales. Detailed account and rationale is provided for the analytical process of assessing the psychometric robustness of the RTSM, with a number of techniques and interpretations relating to component structure and reliability discussed. Agreeability and critique of the two available versions of the RTSM – the bundled and the branched – is also examined. Researchers are encouraged to assist development of the RTSM through further use, taking into account the analysis and recommendations presented.

Rate adaptation for wireless video streaming based on error statistics

This paper presents a new rate-control algorithm for live video streaming over wireless IP networks, which is based on selective frame discarding. In the proposed mechanism excess 'P' frames are dropped from the output queue at the sender using a congestion estimate based on packet loss statistics obtained from RTCP feedback and from the Data Link (DL) layer. The performance of the algorithm is evaluated through computer simulation. This paper also presents a characterisation of packet losses owing to transmission errors and congestion, which can help in choosing appropriate strategies to maximise the video quality experienced by the end user. Copyright © 2007 Inderscience Enterprises Ltd.

Peer-to-peer video streaming

The Internet as a video distribution medium has seen a tremendous growth in recent years. Currently, the transmission of major live events and TV channels over the Internet can easily reach hundreds or millions of users trying to receive the same content using very distinct receiver terminals, placing both scalability and heterogeneity challenges to content and network providers. In private and well-managed Internet Protocol (IP) networks these types of distributions are supported by specially designed architectures, complemented with IP Multicast protocols and Quality of Service (QoS) solutions. However, the Best-Effort and Unicast nature of the Internet requires the introduction of a new set of protocols and related architectures to support the distribution of these contents. In the field of file and non-real time content distributions this has led to the creation and development of several Peer-to-Peer protocols that have experienced great success in recent years. This chapter presents the current research and developments in Peer-to-Peer video streaming over the Internet. A special focus is made on peer protocols, associated architectures and video coding techniques. The authors also review and describe current Peer-to-Peer streaming solutions. © 2013, IGI Global.

Evaluation of the H.264 scalable video coding in error prone IP networks

The Joint Video Team, composed by the ISO/IEC Moving Picture Experts Group (MPEG) and the ITU-T Video Coding Experts Group (VCEG), has standardized a scalable extension of the H.264/AVC video coding standard called Scalable Video Coding (SVC). H.264/SVC provides scalable video streams which are composed by a base layer and one or more enhancement layers. Enhancement layers may improve the temporal, the spatial or the signal-to-noise ratio resolutions of the content represented by the lower layers. One of the applications, of this standard is related to video transmission in both wired and wireless communication systems, and it is therefore important to analyze in which way packet losses contribute to the degradation of quality, and which mechanisms could be used to improve that quality. This paper provides an analysis and evaluation of H.264/SVC in error prone environments, quantifying the degradation caused by packet losses in the decoded video. It also proposes and analyzes the consequences of QoS-based discarding of packets through different marking solutions.