NDLI: Genetic CONDENSATION for motion tracking

Content Provider	IEEE Xplore Digital Library
Author	Zhu Ye Zhi-Qiang Liu
Copyright Year	2005
Description	Author affiliation: Sch. of Creative Media, City Univ. of Hong Kong, Kowloon, China (Zhu Ye; Zhi-Qiang Liu)
Abstract	Tracking is a particularly important issue in human motion analysis since it serves as a means to prepare data for pose estimation and action recognition. The CONDENSATION algorithm is a kind of conditional density propagation method for motion tracking. This algorithm combines factored sampling with learned dynamic models to propagate an entire probability distributes for object position and shape over time. It can accomplish highly robust tracking of object motion. However, it usually requires a large number of samples to ensure a fair maximum likelihood estimation of the current state. The important problem of the CONDENSATION algorithm is to choose proper samples to approach the actual samples position. In this paper, we use the mutation and crossover operators of the genetic algorithm to find more appropriate samples by calculating weights. Accordingly, we can solve the heavy demand of samples in the CONDENSATION algorithm. Eventually, we can improve robustness, accuracy and flexibility in CONDENSATION for visual tracking.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	5542
Ending Page	5547
File Size	1638975
Page Count	6
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527924
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tracking Robustness Humans Motion analysis Motion estimation Sampling methods Shape Maximum likelihood estimation Genetic mutations Genetic algorithms Metroplis algorithm CONDENSATION algorithm fictored samnpling Genetic algorithm
Content Type	Text
Resource Type	Article

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