Open Access
Issue
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
Article Number 01001
Number of page(s) 14
DOI https://doi.org/10.1051/epjconf/202532801001
Published online 18 June 2025
  1. K. Duvvuri, H. Kanisettypalli, K. Jaswanth, M., Video Classification Using CNN and Ensemble Learning, in Proc. 9th Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), 2023. [Google Scholar]
  2. M. Ramesh, K. Mahesh, A Performance Analysis of Pre-trained Neural Network and Design of CNN for Sports Video Classification, in Proc. Int. Conf. Commun. Signal Process., July 28-30, 2020. [Google Scholar]
  3. Y. Luo, B. Yang, Video motion classification based on CNN, in Proc. IEEE Int. Conf. Comput. Sci. Artif. Intell. Electron. Eng. (CSAIEE), 2021. [Google Scholar]
  4. M. Liu, Video Classification Technology Based on Deep Learning, in Proc. Int. Conf. Inf. Sci. Parallel Distrib. Syst. (ISPDS), 2020. [Google Scholar]
  5. M. Sharma, S.K. Singh, S. Singhal, Real-time video surveillance using Pyramidal CNN, in Proc. 2nd Int. Conf. Disruptive Technol. (ICDT), 2024. [Google Scholar]
  6. A.A. Tiriya, M.A. Zaveri, Human Behaviour Classification for Video Surveillance Using CNN, in Proc. 2nd Int. Conf. Adv. Comput. Commun. Control Netw. (ICACCCN), 2020. [Google Scholar]
  7. P. Kapoor, A Video Surveillance Detection of Moving Object Using Deep Learning, in Proc. 3rd Int. Conf. Smart Gener. Comput. Commun. Netw. (SMART GENCON), Karnataka, India, Dec. 29-31, 2023. [Google Scholar]
  8. S. M. Banti, G. S. Banti, J.R. Fenitha, S. R. Banti, Fight Detection in Surveillance Video Dataset Versus Real-time Surveillance Video Using 3DCNN and CNN-LSTM, in Proc. Int. Conf. Comput. Power Commun. (ICCPC), 2022. [Google Scholar]
  9. A. Dhiman, M. Deshmukh, Optimized Approach for Video Summarization Using Transfer Learning and LSTM, in Proc. Int. Conf. Comput. Intell. Sustain. Eng. Solut. (CISES), 2023. [Google Scholar]
  10. Y. Zhao, R. An, D. Ou, A Video Capturing and Processing Platform Based on Mobile Edge Computing, in Proc. IEEE Int. Conf. Comput. Sci. Artif. Intell. Electron. Eng. (CSAIEE), 2020. [Google Scholar]
  11. A. Karjauv, S. Bakhtiyarov, C. Zhang, J.-C. Bazin, I.S. Kweon, Motionsnap: A Motion Sensor-Based Approach for Automatic Capture, in Proc. IEEE Int. Conf. Multimedia Expo (ICME), 2021. [Google Scholar]
  12. S.E. Adi, A.J. Casson, Design and Optimization of a TensorFlow Lite Deep Learning Neural Network for Human Activity Recognition on a Smartphone, in Proc. 43rd Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Oct. 31 - Nov. 4, 2021. [Google Scholar]
  13. F.A. Mahdi, D.S. Prasvita, Theresiawati, Implementation of Convolutional Neural Network Algorithm for Android-Based Classification of Taekwondo Martial Arts, in Proc. Int. Conf. Informatics Multimedia Cyber Inf. Syst. (ICIMCIS), 2022. [Google Scholar]
  14. B.P.D. Pitranto, R. Saptoto, O.C. Jakaria, W. Andriyani, A Comparative Study of Java and Kotlin for Android Mobile Application Development, in Proc. 3rd Int. Semin. Res. Inf. Technol. Intell. Syst. (ISRITI), 2020. [Google Scholar]
  15. M.S. Minu, C.R.A, Deep Learning-Based Aerial Image Classification Model Using Inception with Residual Network and Multilayer Perceptron, in Microprocessors and Microsystems, 2022. [Google Scholar]
  16. L.C. Ho, M.A. Ismail, Android Application for Posture Analysis Using TensorFlow and Computer Vision, in Proc. Int. Conf. Softw. Eng. Comput. Syst. & 4th Int. Conf. Comput. Sci. Inf. Manag. (ICSECS-ICOCSIM), 2021. [Google Scholar]
  17. M.C. Schiappa, N. Biyani, P. Kamtam, S. Vyas, H. Palangi, V. Vineet, Y. Rawat, A Large-scale Robustness Analysis of Video Action Recognition Models, in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2023. [Google Scholar]
  18. D. Kolosov, V. Kelefouras, P. Kourtessis, I. Mporas, Anatomy of DL Image Classification & Object Detection on Commercial Edge Devices, in IEEE Access, Sept. 19, 2022, accepted Oct. 10, 2022, published Oct. 13, 2022. [Google Scholar]
  19. W. Pratumthong, N. Phinyosab, P. Saiyut, S. Prongnuch, Mobile Application for Basic Computer Troubleshooting Using TensorFlow Lite, in Proc. 7th Int. Conf. Eng. Appl. Sci. Technol. (ICEAST), 2021. [Google Scholar]
  20. H. Giedra, D. Matuzevicius, Predicting Time Complexity of TensorFlow Lite Models, in Proc. IEEE Open Conf. Electr. Electron. Inf. Sci. (eStream), 2024. [Google Scholar]
  21. R.S. Praneeth, K.C.S. Akash, B.K. Sree, P.I. Rani, A. Bhola, Scaling Object Detection to the Edge with YOLOv4, TensorFlow Lite, in Proc. 7th Int. Conf. Comput. Methodol. Commun. (ICCMC), 2023. [Google Scholar]
  22. V.H.N. Banti, R. Rajani, N.C. Gowda, D.R. Ramani, Two-Stage Video Classification Approach Using Convolution Neural Network, in Proc. 2nd Int. Conf. Intell. Data Commun. Technol. Internet Things (IDCIoT), 2024. [Google Scholar]
  23. M.M. Khan, R. Anwar, F.A. Tanve, D. Shakil, M. Banik, S.K. Gupta, Development of Web and Mobile-Based Smart Online Healthcare System, in Proc. IEEE 12th Annu. Ubiquitous Comput. Electron. Mobile Commun. (UEMCON), 2021. [Google Scholar]
  24. Y. Xian, B. Korbar, M. Douze, L. Torresani, B. Schiele, Z. Akata, Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation, in IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 12, Dec. 2022. [Google Scholar]
  25. R. Zhou, X. Chen, An Improved MoViNet Algorithm for Lightweight Video Recognition, in Proc. 15th Int. Conf. Adv. Comput. Intell. (ICACI), 2023. [Google Scholar]
  26. M.R. Islam, A.B.M.R. Uz Zaman, F.-A. Islam, F. Tabassum, M.A. Israk, M.S. Mahmud, J. Majumder, Crime Prediction by Detecting Violent Objects and Activity Using Pre-trained YOLOv8n and MoViNetA0 Models, in Proc. Int. Conf. Model. E-Inf. Res. Artif. Learn. Digit. Appl. (ICMERALDA), 2023. [Google Scholar]
  27. J. Arthy, S.D. Variar, N. Tharakeshwar, R.A. Narayan, End-to-End Network Traffic Examination for Network Intrusion Detection using Feature Embedding Learning, in Proc. 4th Int. Conf. Intell. Technol. (CONIT), 2024. [Google Scholar]
  28. D. Kondratyuk, L. Yuan, Y. Li, L. Zhang, M. Tan, M. Brown, B. Gong, MoViNets: Mobile Video Networks for Efficient Video Recognition, in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2021. [Google Scholar]

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