3D Part-Based Sparse Tracker with Automatic Synchronization and Registration

Type
Conference Paper

Authors
Bibi, Adel
Zhang, Tianzhu
Ghanem, Bernard

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Visual Computing Center (VCC)

Online Publication Date
2016-12-13

Print Publication Date
2016-06

Date
2016-12-13

Abstract
In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused in the motion model. Also, since 3D trackers are sensitive to synchronization and registration noise in the RGB and depth streams, we propose automated methods to solve these two issues. Extensive experiments are conducted on a popular RGBD tracking benchmark, which demonstrate that our tracker can achieve superior results, outperforming many other recent and state-of-the-art RGBD trackers.

Citation
Bibi A, Zhang T, Ghanem B (2016) 3D Part-Based Sparse Tracker with Automatic Synchronization and Registration. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/CVPR.2016.160.

Acknowledgements
Research in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Journal
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

DOI
10.1109/CVPR.2016.160

Additional Links
http://ieeexplore.ieee.org/document/7780529/

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