STC Tracking Algorithm Based on Kalman Filter

DOI

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.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Panqiao Chen
AU  - Mengzhao Yang
PY  - 2016/03
DA  - 2016/03
TI  - STC Tracking Algorithm Based on Kalman Filter
BT  - Proceedings of the 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  - 10.2991/icmmct-16.2016.382
ID  - Chen2016/03
ER  -