@inproceedings{aa8fa3661d8a4e35890e530135ef542a,
title = "A Deep Learning-Based Approach for Camera Motion Classification",
abstract = "The automatic estimation of the various types of camera motion (e.g., traveling, panning, rolling, zoom..) that are present in videos represents an important challenge for automatic video indexing. Previous research works are mainly based on optical flow estimation and analysis. In this paper, we propose a different, deep learning-based approach that makes it possible to classify the videos according to the type of camera motion. The proposed method is inspired from action recognition approaches and exploits 3D convolutional neural networks with residual blocks. The performances are objectively evaluated on challenging videos, involving blurry frames, fast/slow motion, poorly textured scenes. The accuracy rates obtained (with an average score of 94\%) demonstrate the robustness of the proposed model.",
keywords = "3D CNN, Camera motion classification, Resnet, deep learning",
author = "Kaouther Ouenniche and Ruxandra Tapu and Titus Zaharia",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 9th European Workshop on Visual Information Processing, EUVIP 2021 ; Conference date: 23-06-2021 Through 25-06-2021",
year = "2021",
month = jun,
day = "23",
doi = "10.1109/EUVIP50544.2021.9483961",
language = "English",
series = "Proceedings - European Workshop on Visual Information Processing, EUVIP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "A. Beghdadi and Cheikh, \{F. Alaya\} and J.M.R.S. Tavares and A. Mokraoui and G. Valenzise and L. Oudre and M.A. Qureshi",
booktitle = "Proceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021",
}