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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/37949 |
| Date | 10 June 2023 |
| Creators | Fitzhenry, Charles |
| Contributors | Marais, Patrick, Marquard, Stephen |
| Publisher | Faculty of Science, Department of Computer Science |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MSc |
| Format | application/pdf |
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