Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

STC Tracking Algorithm Based on Kalman Filter

Authors
Panqiao Chen, Mengzhao Yang
Corresponding Author
Panqiao Chen
Available Online March 2016.
DOI
https://doi.org/10.2991/icmmct-16.2016.382How to use a DOI?
Keywords
objecting tracking; occlusion; STC; Kalman Filter
Abstract
During object tracking, Fast tracking via Spatio-Temporal Context Learning which combines temporal correlation among sequential frames and spatial correlation between object and background can solve the problem of semi-occlusion, but not full-occlusion. Kalman Filter makes use of the predictive value and measurement to calculate the optimal state. This paper aims at solving the full-occlusion problems by combining the algorithm and Kalman Filter together. Experiments show that the improved STC can solve occlusion problems effectively.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 4th International Conference on Machinery, Materials and Computing Technology
Part of series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-165-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmmct-16.2016.382How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Panqiao Chen
AU  - Mengzhao Yang
PY  - 2016/03
DA  - 2016/03
TI  - STC Tracking Algorithm Based on Kalman Filter
BT  - 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 1916
EP  - 1920
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.382
DO  - https://doi.org/10.2991/icmmct-16.2016.382
ID  - Chen2016/03
ER  -