Video Classification Results with Artificial Intelligence and Machine Learning
Keywords:Artificial Intelligence , Classification, Video Processing, Machine Learning
The study is related to the classification of the videos of the UCF101 dataset obtained from kaggle with the help of artificial intelligence and machine learning. The ucf 101 dataset has six classes and 155 videos in each class, each of which has approximately 150 picture frames. and with 3 different preprocessing algorithms, features were obtained from each picture frame, and 3 different accuracies were obtained by sending them to the LSTM classifier and the obtained results were compared with each other. In the classification process, cross validation was used to confirm the accuracy obtained.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural computation (1997) 9(8):1735–1780. doi:10.1162/neco.19184.108.40.2065.
Roach MJ, Mason J, Xu L, Stentiford F, Heath M. Recent Trends In Video Analysis: A Taxonomy Of Video Classification Problems. Proceedings of the 6th International Conference on Internet and Multimedia Systems and Applications (IASTED) (2002).
Christoph, F., Haoqi, F, Jitendra M, Kaiming H. SlowFast Networks for Video Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019). p. 6202–6211.
LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based Learning Applied to Document Recognition. Intelligent Signal Processing (2001) 86(11):2278–2324. doi:10.1109/5.726791.
Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Advanced Neural Information Processing Systems (2012) 2:1097–1105. doi:10.1145/3065386.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations (2015). p. 1–14.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2015). p. 1–9.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2016). p. 770–778.
Huang G, Liu Z, Van L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (2017). p. 2261–2269.
Yang K, Qinami K, Fei-Fei L, Deng J, Russakovsky O. Towards fairer datasets. In: Hildebrandt M, Castillo C, Celis E, Ruggieri S, Taylor L, Zanfir-Fortuna G, editors. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM (2020). p. 547–558.
Chollet F. Python ile Derin Öğrenme [Deep Learning with Python]. Ankara: Buzdağı Yayınevi (2019). 1–52.
Ayyüce Kızrak M, Bolat B. Derin Öğrenme ile Kalabalık Analizi Üzerine Detaylı Bir Araştırma [A Comprehensive Survey of Deep Learning in Crowd Analysis]. Bilişim Teknolojileri Dergisi (2018) 11(3):263–286. doi:10.17671/gazibtd.419205.
Shervine, A, Afshine, A. Recurrent Neural Networks cheatsheet. Lecture Notes for the Course CS230:Deep Learning (2022).
Irene A, Gianmarco B, Francesco L. Image and Video Forensics. Journal of Imaging (2021):7–242. doi:10.3390/jimaging7110242.
Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN computer science (2021) 2(3):160. doi:10.1007/s42979-021-00592-x.
Walther J. Hierarchical Electrical Load Forecasting of Industrial Production Systems in the Manufacturing Industry based on Deep Learning. PhD Thesis. Fachbereich Maschinenbau an der Technischen Universität Darmstadt. Darmstadt (2022). doi:10.26083/TUPRINTS-00021767.
Dimitrova N, Agnihotri L, Wei G. Video classification based on HMM using text and faces. In: 10th European Signal Processing Conference (2000). p. 1–4.
Abhale AB, Manivannan S.S. Deep Learning Algorithmic Approach for Operational Anomaly Based Intrusion Detection System in Wireless Sensor Networks. Pre-Print at Research Square (2021):1–29. doi:10.21203/rs.3.rs-777010/v1.
Özkara C, Ekim P. Real-Time Facial Emotion Recognition for Visualization Systems. In: 2022 Innovations in Intelligent Systems and Applications Conference (2022). p. 1–5.
Thilagaraj M, Arunkumar N, Petchinathan G. Classification of Breast Cancer Images by Implementing Improved DCNN with Artificial Fish School Model. Computational Intelligence and Neuroscience (2022)(Special Issue: Mental Illness Detection and Analysis on Social Media). doi:10.1155/2022/6785707.
Copyright (c) 2023 Elif Akarsu, Tevhit Karacalı
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The authors keep the copyrights of the published materials with them, but the authors are aggee to give an exclusive license to the publisher that transfers all publishing and commercial exploitation rights to the publisher. The puslisher then shares the content published in this journal under CC BY-NC-ND license.