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In this research, we explore the domain of video labeling using the UCF-101 dataset. The objective is to develop an advanced video processing system for action recognition. Leveraging previous research papers as a foundation, our study aims to enhance video labeling techniques by implementing machine learning algorithms capable of accurately identifying actions within the given video input. By utilizing the UCF-101 dataset, we seek to create an intelligent model that not only extracts relevant information from videos but also provides concise and meaningful label of the actions depicted. This research contributes existing knowledge with innovative approaches, like deep learning and other dataset.
"Video labelling using deep learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.c242-c252, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504252.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator