VIDEO DATA FOR TRANSMISSION IN TV CHANNELS
Article Sidebar
Main Article Content
Аннотация:
Intelligent analysis of video data encompasses a variety of techniques, including machine learning, deep learning, computer vision, and real-time processing. These methods are designed to automate the detection, categorization, and enhancement of video content, ensuring seamless transmission and high-quality viewing experiences. For instance, deep learning models, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are extensively used for content recognition and anomaly detection in video streams[1].
Article Details
Как цитировать:
Библиографические ссылки:
Marios S. Pattichis, Venkatesh Jatla, Alvaro E. ulloa Cerna. “A Review of Machine Learning Methods Applied to Video Analysis Systems”, .2023 https://doi.org/10.48550/arXiv.2312.05352
G. Sreenu, M. A. Saleem Durai “Intelligent video surveillance: a review through deep learning techniques for crowd analysis”. Journal of Big Data. Article number: 48 (2019)
Branimir S. Jaksic, Mile B. Petrovic and Alvaro E. ulloa Cerna. “Implementation of Video Compression Standards in Digital Television”, 2016 http://dx.doi.org/10.5772/64833
G. Cox, An Introduction to Ultra HDTV and HEVC, ATEME, Paris, France, July 2013.
Koushik, J. Understanding Convolutional Neural Networks. May 2016. Available online: http://arxiv.org/abs/1605.09081.
LeCun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 1998, 86, 2278–2324. [Google Scholar] [CrossRef] [Green Version]
Mohammad Mustafa Taye. “Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions”, Computation 2023, 11(3), 52; https://doi.org/10.3390/computation11030052