Deep Neural Networks for Moving Object Classification in Video Surveillance Applications

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.