![]() Once captured, the data is then either stored directly on the capture hardware or sent to a server over a LAN or the Internet. In this case, such slide shows can also be recorded. Sometimes, the lecturer may use visual aids to support their speech, such as slide shows, which are presented to the audience with some kind of projector. Hardware is used to capture the lecturer's voice along with the video of the lecturer. Where lecture recording is done at scale, the recording system may be integrated with the timetabling system and the collection of metadata may be automated. 71% of institutions responding to a UCISA survey in 2016 indicated that this technology was available in their institution. It is widely used in universities and higher education in the UK and Australia to provide support for students. It consists of hardware and software components that work in synergy to record the audio and visual components of the lecture. arXiv:1712.Lecture recording refers to the process of recording and archiving the content of a lecture, conference, or seminar. Ma, D., Zhang, X., Ouyang, X., Agam, G.: Lecture video indexing using boosted margin maximizing neural networks. Tuna, T.: Automated Lecture Video Indexing With Text Analysis and Machine Learning. 60, 34–44 (2012)Ĭhand, D.: Lecture video segmentation using speech content. Gayathri, N., Mahesh, K.: A systematic study on video indexing. Gaikwad H., Hapase, A., Kelkar, C., Khairnar, N.: News video segmentation and categorization using text extraction technique. Lin, M., Chau, M., Cao, J., Nunamaker, J.F., Jr.: Automated video segmentation for lecture videos: a linguistic based approach. Haviana, S.F.C., Kurniadi, D.: Average hashing for perceptual image similarity in mobile phone application. Ravinder, M., Venugopal, T.: Content-based video indexing and retrieval using key frames texture, edge and motion features. In: IEEE Transactions on Circuits and Systems for Video Technology. Huang, C., Liao, B.: A robust scene-change detection method for video segmentation. In: 2015 IEEE Frontiers in Education Conference. Tuna, T., Joshi, M., Varghese, V., Deshpande, R., Subhlok, J., Verma, R.: Topic based segmentation of classroom videos. In: 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT), pp. Īğzıyağlı, V.S., Oğul, H.: Multi-level lecture video classification using text content. (eds.) Video Segmentation and Its Applications, pp. Li, H., Ngan, K.N.: Image/Video segmentation: current status, trends, and challenges. Wang, W., Zhou, T., Porikli, F., Crandall, D., Van Gool, L.: A survey on deep learning technique for video segmentation. Scagnoli, N.I., Choo, J., Tian, J.: Students’ insights on the use of video lectures in online classes. Pokhrel, S., Chhetri, R.: A literature review on impact of COVID-19 pandemic on teaching and learning. With further evaluation, this system can potentially be proven to be convenient enough to be introduced as a new tool in the educational industry to supplement online learning. The system developed in this research provides both lecturers and students with a method to label and segment their videos based on key topics automatically. It obtained 91% accuracy and an 80% F1 score, which is indicative of its reliability in determining whether a slide is a topic transition or not. The classification model’s performance was satisfactory. ![]() The video undergoes a series of preprocessing steps that gradually cut down the group of frames to contain only distinct slides before inputting them into the model. Topic transitions were determined using a Convolutional Neural Network - based Binary Classification Model trained on an original dataset of lecture videos collected from different educational resources. However, this research investigates the use of lecture video’s presentation slides to determine topics. ![]() Previous research on this topic have placed heavy emphasis on the lecturer’s speech to segment the video by topic. To address this issue, a system that takes any slide-based lecture videos as input and outputs a list of all the topic transitions contained in the video was developed. ![]() Unfortunately, manually segmenting videos requires time and effort, adding even more to their workload. One of the ways teachers can provide more engaging lectures online is by segmenting recorded lectures by topic, which allows students to interact with the video and navigate it. The recent rise in popularity of online learning has completely overhauled the way students consume informative content and how educators provide it. ![]()
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