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

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.
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Issue Date
2014-01
Language
ENG
Keywords

ALGORITHM; MODEL; MANIPULATION; REGISTRATION; MIXTURE

Citation

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v.25, no.1, pp.108 - 121

ISSN
1047-3203
DOI
10.1016/j.jvcir.2013.03.020
URI
http://hdl.handle.net/10203/189456
Appears in Collection
ME-Journal Papers(저널논문)
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