Target Tracking Based on Mean-shift and Kalman Filter
- 10.2991/asei-15.2015.257How to use a DOI?
- target tracking; the mean deviation; Kalman filter; Kernel function histogram.
Analysis of the scheme - shift is difficult to effectively track the main defect of gray level moving targets un-der complicated background, puts forward the combination of Mean shift and kalman filter method for target tracking. The method by using kalman filter to predict the target in the current moment of starting position, then scheme - the shift in the location of the neighborhood looking for targets within the location. At the same time, use Bhattacharyya coefficient measurement "target model" and "candidate model" similarity degree, determine whether "candidate model" is replaced by "target model", to avoid excessive update target model. On the background of object plane target image sequence test results show that this method is compared with the original scheme - shift method can obviously improve the stability of target tracking.
- © 2015, 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 - Songtao Jiang PY - 2015/05 DA - 2015/05 TI - Target Tracking Based on Mean-shift and Kalman Filter BT - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation PB - Atlantis Press SP - 1308 EP - 1312 SN - 2352-5401 UR - https://doi.org/10.2991/asei-15.2015.257 DO - 10.2991/asei-15.2015.257 ID - Jiang2015/05 ER -