Browsing by Author "Marquard, Stephen"

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    Fast presenter tracking for 4K lecture videos using computationally inexpensive algorithms
    (2023) Fitzhenry, Charles; Marais, Patrick; Marquard, Stephen
    Lecture recording has become an essential tool for educational institutions to enhance the student learning experience and offer online courses for remote learning programs. Highresolution 4K cameras have gained popularity in these systems due to their affordability and clarity of written content on boards/screens. Unfortunately, at 4K resolution, a typical 45- minute lecture video easily exceeds 2GB. Many video files of this size place a financial burden on institutions and students, especially in developing countries where financial resources are limited. Institutions require costly high-end equipment to capture, store and distribute this ever-increasing collection of videos. Students require a fast internet connection with a large data quota for off-campus viewing, which can be too expensive for many, especially if they use mobile data. This project designs and implements a low-cost presenter and writing detection front-end that can integrate with an external Virtual Cinematographer (VC). Gesture detection was also explored; however, the frame differencing approach used for presenter detection was not sufficiently robust for gesture detection. Our front-end is carefully designed to run on commodity computers without requiring expensive Graphics Processing Units (GPU) or servers. An external VC can use our contextual information to segment a smaller cropping window from the 4K frame, only containing the presenter and relevant boards, drastically reducing the file size of the resultant videos while preserving writing clarity. The software developed as part of this project will be available as open source. Our results show that the front-end module is fit for purpose and sufficiently robust across several challenging lecture venue types. On average, a 2-minute video clip is processed by the front-end in under 60 seconds (or approximately half of the input video duration). The majority (89%) of this time is used for reading and decoding frames from storage. Additionally, our low-cost presenter detection achieves an overall F1-Score of 0.76, while our writing detection achieves an overall F1-Score of 0.55. We also demonstrate a mean reduction of 81.3% in file size from the original 4K video to a cropped 720p video when using our front-end in a full pipeline with an external VC.
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    From Gatekeepers to Gateways: Courses Impeding Graduation Annual Report 2019
    (University of Cape Town, 2020) Shay, Suellen; Collier-Reed, Brandon; Hendry, Jane; Marquard, Stephen; Kefale, Kende; Prince, Robert; Steyn, Sanet; Mpofu-Mketwa, Tsitsi; Carstens, Rondine
    The Courses Impeding Graduation (CIG) Project is a research and development initiative of the Centre for Higher Education Development (CHED) addressing the problem of high failure rates in courses that are obstacles to student retention and progression. This report report lays out the background, aims, objectives, and outcomes of the project in 2019, with a particular focus on first-year Mathematics courses in the Faculty of Science, examining which students are at higher risk of failing these courses. The report includes student perspectives gathered through focus groups.
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    Improving Searchability of Automatically Transcribed Lectures Through Dynamic Language Modelling
    (2012) Marquard, Stephen
    Recording university lectures through lecture capture systems is increasingly common. However, a single continuous audio recording is often unhelpful for users, who may wish to navigate quickly to a particular part of a lecture, or locate a specific lecture within a set of recordings. A transcript of the recording can enable faster navigation and searching. Automatic speech recognition (ASR) technologies may be used to create automated transcripts, to avoid the significant time and cost involved in manual transcription. Low accuracy of ASR-generated transcripts may however limit their usefulness. In particular, ASR systems optimized for general speech recognition may not recognize the many technical or discipline-specific words occurring in university lectures. To improve the usefulness of ASR transcripts for the purposes of information retrieval (search) and navigating within recordings, the lexicon and language model used by the ASR engine may be dynamically adapted for the topic of each lecture. A prototype is presented which uses the English Wikipedia as a semantically dense, large language corpus to generate a custom lexicon and language model for each lecture from a small set of keywords. Two strategies for extracting a topic-specific subset of Wikipedia articles are investigated: a naïve crawler which follows all article links from a set of seed articles produced by a Wikipedia search from the initial keywords, and a refinement which follows only links to articles sufficiently similar to the parent article. Pair-wise article similarity is computed from a pre-computed vector space model of Wikipedia article term scores generated using latent semantic indexing. The CMU Sphinx4 ASR engine is used to generate transcripts from thirteen recorded lectures from Open Yale Courses, using the English HUB4 language model as a reference and the two topic-specific language models generated for each lecture from Wikipedia.
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    Improving searchability of automatically transcribed lectures through dynamic language modelling
    (2012) Marquard, Stephen; Mbogho, Audrey J W
    Recording university lectures through lecture capture systems is increasingly common. However, a single continuous audio recording is often unhelpful for users, who may wish to navigate quickly to a particular part of a lecture, or locate a specific lecture within a set of recordings. A transcript of the recording can enable faster navigation and searching. Automatic speech recognition (ASR) technologies may be used to create automated transcripts, to avoid the significant time and cost involved in manual transcription.
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    Investigating the virtual directing strategies of a virtual cinematographer in an automatic lecture video post-processing system
    (2023) Khatieb, Mohamed Tanweer; Marais, Patrick ; Marquard, Stephen; Marquard, Stephen
    As recording technology improves and becomes more affordable, many learning institutions are using lecture recording to make lessons more persistent and accessible. Statically mounted 4K cameras are now cheaper than PTZ cameras which makes them a desirable alternative for lecture recordings. Unfortunately, 4K resolution videos are very large, posing a problem for storage and streaming - the file size for a 45 - 60 minute lecture video in 4K can exceed 2GB. Many students cannot afford the bandwidth required to stream such large files. Furthermore, since static 4K cameras do not move, they require a wide-angle view of the venue in order to capture as much of the front of the venue as possible. This view is much too zoomed out for viewers to see the details, such as writing on the boards and the presenter's facial expressions, captured by the 4K resolution. This dissertation investigates an approach to post-processing these 4K lecture videos to reduce the file size and emphasise lecture details such as lecture motion and board/screen usage. This is done using scene tracking data (generated via a third-party front-end) which a Virtual Cinematographer (VC) uses to make decisions on about which areas to crop from each 4K frame in the original video. The VC then positions and sizes the cropping windows in such a way that the resultant, cropped video resembles one recorded by a human camera operator. This is accomplished using cinematographic heuristics to inform its decision-making. The VC uses scene analysis algorithms to determine how the environment changes as time progresses in the video. By dividing the video into “chunks” (equivalent to “scenes” in traditional cinematography) based on context, the VC is able to maintain stable shots with consistent framing to avoid jittery and disorienting footage. These contextual chunks are determined by comparing the trajectory of the presenter with the manner in which the features on the board regions change over time. After the chunks are established, the VC creates transitions between them while avoiding any changes to the framing inside each chunk. The final output is a JSON file containing the cropping coordinates for each frame in the video for a third-party video cropping application to use when producing the final video. We performed a user evaluation of the VC to measure user satisfaction with the resulting output videos and how successful it was at following its heuristics. The VC succeeded in following the major heuristics such that viewers were satisfied with the output based on the framing of the presenter and the content on the boards, transition stability and smoothness of motion, and transition frequency with the VC only changing shots when necessary.
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    Review of Open Educational Resources
    (2012-10) Hodgkinson-Williams, Cheryl; Marquard, Stephen
    This presentation provides an overview of OER in general as well as in South Africa.
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    UCT ERT Student Experience Survey 2020
    (University of Cape Town, 2020-09-25) Marquard, Stephen; Walji, Sukaina; Lester, Soraya; Kefale, Kende; Deacon, Andrew
    Final report of the UCT Emergency Remote Teaching Student Experience Survey 2020, an online survey of student experiences of emergency remote teaching (ERT) during the 2nd term of 2020 (April to July 2020). The purpose of the survey was to inform and improve the design of courses taught online during the second semester of 2020 (August to November), and improve support for students where possible. The survey was thus a form of institutional research and followed an exploratory research design rather than setting out to confirm or disprove specific hypotheses. The report presents key concerns of students during this time period, including mental health, course workload in relation to available time, and challenges relating to course site design, assessments, social connectedness, Internet access and mobile data and preferences relating to video material and synchronous teaching. While most students experienced some difficulties arising from ERT and the COVID-19 lockdown conditions, students who no longer had access to UCT residences after the start of ERT were particularly adversely affected.
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    UCT Laptop Project Report and Appendices
    (2016-02-24) Brown, Cheryl; Chernotsky, Kira; Marquard, Stephen; Fellingham, Kevin
    In 2017 the University of Cape Town made a decision to roll out a programme to provide a new laptop to every first year undergraduate student fully funded through the National Student Financial Aid Scheme see http://mg.co.za/article/2017-02-16-00-flip-varsity-lectures-for-equal-success/ Over a period of fours year between 2013 and 2016, four courses at UCT (PHY1004W, CHE1005W, RDL 1008H/9H and AGP2039W) piloted the use of laptops in both formal and informal teaching and learning. This report summarizes the lessons learnt from this pilot programme.