Target Tracking Based on Mean-shift and Kalman Filter

DOI

10.2991/asei-15.2015.257How to use a DOI?

Keywords

target tracking; the mean deviation; Kalman filter; Kernel function histogram.

Abstract

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.

Copyright

© 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/).

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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  -