Volumetric 3D Tracking by Detection - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2016

Volumetric 3D Tracking by Detection

Abstract

In this paper, we propose a new framework for 3D tracking by detection based on fully volumetric representations. On one hand, 3D tracking by detection has shown robust use in the context of interaction (Kinect) and surface tracking. On the other hand, volumetric representations have recently been proven efficient both for building 3D features and for addressing the 3D tracking problem. We leverage these benefits by unifying both families of approaches into a single, fully volumetric tracking-by-detection framework. We use a centroidal Voronoi tessellation (CVT) representation to compactly tessellate shapes with optimal discretization, construct a feature space, and perform the tracking according to the correspondences provided by trained random forests. Our results show improved tracking and training computational efficiency and improved memory performance. This in turn enables the use of larger training databases than state of the art approaches, which we leverage by proposing a cross-tracking subject training scheme to benefit from all subject sequences for all tracking situations, thus yielding better detection and less overfitting.
Fichier principal
Vignette du fichier
Huang_Allain_CVPR16.pdf (3.14 Mo) Télécharger le fichier
Vignette du fichier
thumbnail.jpg (19.78 Ko) Télécharger le fichier
supplemental.pdf (176.68 Ko) Télécharger le fichier
video.mp4 (33.36 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Format Figure, Image
Origin Files produced by the author(s)
Origin Files produced by the author(s)
Format Video
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01300191 , version 1 (20-06-2016)

Licence

Identifiers

Cite

Chun-Hao Huang, Benjamin Allain, Jean-Sébastien Franco, Nassir Navab, Slobodan Ilic, et al.. Volumetric 3D Tracking by Detection. CVPR 2016 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2016, Las Vegas, United States. pp.3862-3870, ⟨10.1109/CVPR.2016.419⟩. ⟨hal-01300191⟩
999 View
662 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More