STC Tracking Algorithm Based on Kalman Filter
Panqiao Chen, Mengzhao Yang
Available Online March 2016.
- 10.2991/icmmct-16.2016.382How to use a DOI?
- objecting tracking; occlusion; STC; Kalman Filter
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.
- © 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/).
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 - 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 -