Abstract
Convolutional neural networks (CNNs) are widely used in computer vision-based problems. Video and image data are dominating the Internet. This has led to extensive use of deep learning (DL)-based models in solving tasks like image recognition, image segmentation, video classification, etc. Encouraged by the enhanced performance of CNNs, we have developed ED-NET in order to classify videos as teaching videos or non-teaching videos. Along with the model, we have developed a novel dataset, Teach-VID, containing teaching videos. The data is collected through our e-learning platform Gyaan, an online end-to-end teaching platform developed by our organization, GahanAI. The purpose is to make sure we can restrict non-teaching videos from being played on our portal. The models proposed along with the dataset provide benchmarking results. There are two models presented one that makes use of 3D-CNN and the other uses 2D-CNN and LSTM. The results suggest that the models can be used in real-time settings. The model based on 3D-CNN has reached an accuracy of 98.87%, and the model based on 2D-CNN has reached an accuracy of 96.34%. The loss graph of both models suggests that there is no issue of overfitting and underfitting. The proposed model and dataset can provide useful results in the field of video classification regarding teaching versus non-teaching videos.
Supported by Organization GahanAI, Bengaluru, India
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Gautam, A., Hazra, S., Verma, R., Maji, P., Balabantaray, B.K. (2023). ED-NET: Educational Teaching Video Classification Network. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_12
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