Incremental object learning and robust tracking of multiple objects from RGB-D point set data

Seongyong Koo, Dongheui Lee, Dong Soo Kwon

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

13 Zitate (Scopus)

Abstract

In this paper, we propose a novel model-free approach for tracking multiple objects from RGB-D point set data. This study aims to achieve the robust tracking of arbitrary objects against dynamic interaction cases in real-time. In order to represent an object without prior knowledge, the probability density of each object is represented by Gaussian mixture models (GMM) with a tempo-spatial topological graph (TSTG). A flexible object model is incrementally updated in the pro-posed tracking framework, where each RGB-D point is identified to be involved in each object at each time step. Furthermore, the proposed method allows the creation of robust temporal associations among multiple updated objects during split, complete occlusion, partial occlusion, and multiple contacts dynamic interaction cases. The performance of the method was examined in terms of the tracking accuracy and computational efficiency by various experiments, achieving over 97% accuracy with five frames per second computation time. The limitations of the method were also empirically investigated in terms of the size of the points and the movement speed of objects.

OriginalspracheEnglisch
Seiten (von - bis)108-121
Seitenumfang14
FachzeitschriftJournal of Visual Communication and Image Representation
Jahrgang25
Ausgabenummer1
DOIs
PublikationsstatusVeröffentlicht - Jan. 2014
Extern publiziertJa

Fingerprint

Untersuchen Sie die Forschungsthemen von „Incremental object learning and robust tracking of multiple objects from RGB-D point set data“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren