NDLI: Research of kernel particle filtering target tracking algorithm based on multi-feature fusion

Content Provider	IEEE Xplore Digital Library
Author	Hongxia Chu Kejun Wang
Copyright Year	2010
Description	Author affiliation: College of automation, Harbin Engineering University, 150001, Heilongjiang Province, China (Hongxia Chu; Kejun Wang)
Abstract	The standard particle filter usually fails in the scene of small system noise or weak dynamic models. The robustness of majority tracking algorithm is not high due to using only a single target feature. An efficient multi-feature fusion tracking method was proposed. The article presents the integration of color distributions into kernel particle filtering(KPF) framework, which has typically been used in combination with edge-based image features. The KPF invokes kernels to form a continuous estimate of the posterior density function. Kernel particle filter reasonably allocated particles by improving sampling efficiency. Experiments results show that other features can still stable and reliable track targets when a feature loses identification capabilities of target in the background clutter. Algorithm is simple and high robustness. It can be effectively applied to track target in the complex context.
Starting Page	6189
Ending Page	6194
File Size	1611914
Page Count	6
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5554425
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Kernel Color Image color analysis Image edge detection Histograms Edge feature Kernel particle filter Object tracking Multi-feature Color histograms
Content Type	Text
Resource Type	Article

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