Anomaly Detection in Television Digital Channel
Abstract
Detecting anomalies in industrial processes is a field in constant advancement. However, automating this task presents significant challenges due to the complexity of the problem. In this paper, Deep Learning techniques were employed to detect anomalies in video footage during digital channel testing of televisions on a production line. A 3D Convolutional Neural Network was trained on a dataset containing two classes of videos: those with simulated defects and those without defects. The resulting model achieved an accuracy of 98,45% with a processing speed of 648 FPS.
Keywords:
Anomaly detection, Deep Learning, 3D Convolutional Neural Networks
References
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V. Saligrama and Z. Chen, “Video anomaly detection based on local statistical aggregates,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 2112–2119.
R. Nayak, U. C. Pati, and S. K. Das, “A comprehensive review on deep learning-based methods for video anomaly detection,” Image and Vision Computing, p. 104078, 2020.
W. Sultani, C. Chen, and M. Shah, “Real-world anomaly detection in surveillance videos,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
J. Ren, F. Xia, Y. Liu, and I. Lee, “Deep video anomaly detection: Opportunities and challenges,” in 2021 International Conference on Data Mining Workshops (ICDMW), 2021, pp. 959–966.
Y. Zhao, B. Deng, C. Shen, Y. Liu, H. Lu, and X.-S. Hua, “Spatio-temporal autoencoder for video anomaly detection,” 2017.
Z. Yang, J. Liu, Z. Wu, P. Wu, and X. Liu, “Video event restoration based on keyframes for video anomaly detection,” in IEEE/CVFCVPR, 2023, pp. 14 592–14 601.
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S. Aigner and M. Körner, “Futuregan: Anticipating the future frames of video sequences using spatio-temporal 3d convolutions in progressively growing gans,” arXiv preprint arXiv:1810.01325, 2018.
Published
2024-11-06
How to Cite
FABRICIO, Romulo; PIMENTEL, Agemilson; BELEM, Ruan; SOUSA, Anderson; MARTINHO, Laura; ARAÚJO, Leo; SILVA, Luan; SOUSA, Osmar.
Anomaly Detection in Television Digital Channel. In: WORKSHOP ON COMPUTATIONAL VISION (WVC), 19. , 2024, Rio Paranaíba/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 13-18.
DOI: https://doi.org/10.5753/wvc.2024.34006.
