Authors:
Tiago Silva
1
;
Luís Magalhães
1
;
Manuel Ferreira
2
;
Salik Ram Khanal
2
and
Jorge Silva
2
Affiliations:
1
Centro ALGORITMI, University of Minho, Guimarães, Portugal
;
2
Neadvance, Braga, Portugal
Keyword(s):
Deep Learning, 3D Tracking, Deformable Objects, RGB-D Data, Object Segmentation.
Abstract:
3D object tracking is a topic that has been widely studied for several years. Although there are already several robust solutions for tracking rigid objects, when it comes to deformable objects the problem increases in complexity. In recent years, there has been an increase in the use of Machine / Deep Learning techniques to solve problems in computer vision, including 3D object tracking. On the other hand, several low-cost devices (like Kinect) have appeared that allow obtaining RGB-D images, which, in addition to colour information, contain depth information. In this paper is proposed a 3D tracking approach for deformable objects that use Machine / Deep Learning techniques and have RGB-D images as input. Furthermore, our approach implements a tracking algorithm, increasing the object segmentation performance towards real time. Our tests were performed on a dataset acquired by ourselves and have obtained satisfactory results for the segmentation of the deformable object.