In many real life scenarios, which span from domestic interactions to industrial manufacturing processes, the objects to be manipulated are non-rigid and deformable, hence, both the location of the object and its deformation have to be tracked. Different methodologies have been applied in literature, using different sensors and techniques for addressing this problem. The main contribution of this paper is to propose a Weightless Neural Network approach for non-rigid deformable object tracking. The proposed approach allows deploying an on-line training on the shape features of the object, to adapt in real-time to changes, and to partially cope with occlusions. Moreover, the use of parallel classifiers trained on the same set of images allows tracking the movements of the objects. In this work, we evaluate the filtering/segmentation performance that is a fundamental step for the correct operation of our approach, in the scenario of pizza making.

Segmentation performance in tracking deformable objects via WNNs

Giordano M;De Gregorio M;
2015

Abstract

In many real life scenarios, which span from domestic interactions to industrial manufacturing processes, the objects to be manipulated are non-rigid and deformable, hence, both the location of the object and its deformation have to be tracked. Different methodologies have been applied in literature, using different sensors and techniques for addressing this problem. The main contribution of this paper is to propose a Weightless Neural Network approach for non-rigid deformable object tracking. The proposed approach allows deploying an on-line training on the shape features of the object, to adapt in real-time to changes, and to partially cope with occlusions. Moreover, the use of parallel classifiers trained on the same set of images allows tracking the movements of the objects. In this work, we evaluate the filtering/segmentation performance that is a fundamental step for the correct operation of our approach, in the scenario of pizza making.
2015
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" - ISASI
Object tracking
weightless neural networks
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/307500
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? ND
social impact