DocumentCode
2664130
Title
Tracking in clutter based on Mean Shift embedded particle filter
Author
Zheng, Lin ; Liu, Quan
Author_Institution
Sch. of Inf. Eng., Wuhan Univ. of Technol., Wuhan, China
Volume
6
fYear
2010
fDate
16-18 April 2010
Abstract
In this paper, we present a new Mean Shift embedded particle filter for visual tracking. Two kinds of Mean Shifts are used. The pixel based Mean Shift is employed to optimize each particle independently and locally. Then the particle based Mean Shift is employed to optimize all the particles dependently. This algorithm is used to track objects in the cluttered environment. The experiments show that the method performs robust in complex situation.
Keywords
Monte Carlo methods; clutter; object detection; particle filtering (numerical methods); clutter tracking; mean shift embedded particle filter; object tracking; sequential Monte Carlo techniques; visual tracking; Filtering theory; Information filtering; Information filters; Monte Carlo methods; Optimization methods; Particle filters; Particle tracking; Probability density function; Robustness; Sliding mode control; Particle Filter; Particle based Mean Shift; Pixel based Mean Shift;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5486215
Filename
5486215
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