Deep neural networks for moving object classification in video surveillance applications

Rania Rebai Boukhriss; Emna Fendri; Mohamed Hammami

doi:10.1117/12.2623796

4 March 2022 Deep neural networks for moving object classification in video surveillance applications

Rania Rebai Boukhriss, Emna Fendri, Mohamed Hammami

Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 120841F (2022) https://doi.org/10.1117/12.2623796
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy

Abstract

The moving object classification is a crucial step for several video surveillance applications whatever in the visible or thermal spectra. It still remains an active field of research considering the diversity of challenges related to this topic mainly in the context of an outdoor scene. In order to overcome several intricate situations, many moving objects classification methods have been proposed in the literature. Particular interest is given to the classes “Pedestrian” and “Vehicle”. In this paper, we have proposed a moving object classification approach based on deep learning methods from visible and infrared spectra. Three series of experiments carried on the challenging dataset “CD.net 2014” have proved that the proposed method reach accurate moving objects classification results when compared to methods based on deep learning and handcrafted features.

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Rania Rebai Boukhriss, Emna Fendri, and Mohamed Hammami "Deep neural networks for moving object classification in video surveillance applications", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 120841F (4 March 2022); https://doi.org/10.1117/12.2623796

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KEYWORDS

Video surveillance

Visible radiation

Infrared radiation

Convolutional neural networks

Feature extraction

Video

Image classification

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