TY - GEN
T1 - A Deep Learning-Based Approach for Camera Motion Classification
AU - Ouenniche, Kaouther
AU - Tapu, Ruxandra
AU - Zaharia, Titus
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/23
Y1 - 2021/6/23
N2 - 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.
AB - 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.
KW - 3D CNN
KW - Camera motion classification
KW - Resnet
KW - deep learning
UR - https://www.scopus.com/pages/publications/85111458759
U2 - 10.1109/EUVIP50544.2021.9483961
DO - 10.1109/EUVIP50544.2021.9483961
M3 - Conference contribution
AN - SCOPUS:85111458759
T3 - Proceedings - European Workshop on Visual Information Processing, EUVIP
BT - Proceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021
A2 - Beghdadi, A.
A2 - Cheikh, F. Alaya
A2 - Tavares, J.M.R.S.
A2 - Mokraoui, A.
A2 - Valenzise, G.
A2 - Oudre, L.
A2 - Qureshi, M.A.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th European Workshop on Visual Information Processing, EUVIP 2021
Y2 - 23 June 2021 through 25 June 2021
ER -