Autonomous object recognition in videos using Siamese Neural Networks

Nawel Medjkoune 1 Frédéric Armetta 2 Mathieu Lefort 2 Stefan Duffner 3
2 SMA - Systèmes Multi-Agents
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : For a robot to be deployed in unconstrained real world environments, it needs to be autonomous. In this preliminary work, we focus on the capacity of an autonomous robot to discover and recognize objects in its visual field. Current existing solutions mainly employ complex deep neural architectures that need to be pre-trained using large datasets in order to be effective. We propose a new model for autonomous and unsupervised object learning in videos that does not require supervised pre-training and uses relatively simple visual filtering. The main idea relies on the saliency-based detection and learning of objects considered similar (thanks to a spatio-temporal continuity). For this purpose the learning of objects is based on a Siamese Neural Network (SNN). We demonstrate the capacity of the SNN to learn a good feature representation despite the deliberately simple and noisy process used to extract candidate objects.
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Nawel Medjkoune, Frédéric Armetta, Mathieu Lefort, Stefan Duffner. Autonomous object recognition in videos using Siamese Neural Networks. EUCognition Meeting (European Society for Cognitive Systems) on "Learning: Beyond Deep Neural Networks", Nov 2017, Zurich, Switzerland. pp.4. ⟨hal-01630163⟩

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