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感谢参与，应助指数 +1
oven1986: 求助人反映应助无效。 2014-05-15 22:54:52
oven1986: 屏蔽内容 2014-05-17 21:29:50
oven1986: 求助人反映应助无效。 2014-05-15 22:54:52
oven1986: 屏蔽内容 2014-05-17 21:29:50
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2楼2014-05-15 12:49:05
amazon2008
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3楼2014-05-20 09:47:17
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sunshan4379: 检索EPI+1, 感谢应助！ 2014-07-11 19:18:33
根号四(sunshan4379代发): 金币+10 2014-07-19 17:30:36
sunshan4379: 检索EPI+1, 感谢应助！ 2014-07-11 19:18:33
根号四(sunshan4379代发): 金币+10 2014-07-19 17:30:36
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Accession number: 14164238 Title: Monocular Video Human Motion Tracking based on Hybrid PSO Authors: Zhang Ben Jun1 Author affiliation: 1 Dept. of Sports & Art, Bengbu Med. Coll., Bengbu, China Source title: Journal of Multimedia Abbreviated source title: J. Multimed. (Finland) Volume: 9 Issue: 1 Publication date: Jan. 2014 Pages: 106-13 Language: English ISSN: 1796-2048 Document type: Journal article (JA) Publisher: Academy Publisher Country of publication: Finland Material Identity Number: DW40-2014-004 Abstract: In this paper, we develop a method for tracking markless human pose in monocular video human motion. The number of required particles will grow exponentially when particle filter is applied to high dimensional tracking problems such as tracking human body poses, and particle filter with partitioned sampling is adopted to deal with this problem. We design a 2D human body model with constraints, and put forward a new adaptive way for fusing color, edge and motion cues together to construct the weighing function of particles. When calculating the likelihood function for each particle, we adaptively choose different templates and features according to the occlusion relationship among correlated body limbs. Thus, the proposed algorithm is capable of dealing with complicated occlusions among body limbs. In addition, we introduce a simplified belief hybrid PSO method to propagate the weights of limb observations to the corresponding particles along the edges of the body model, which can make a set of particles carry multiple constraints. Then we test the method on three video sequences which contain heavy background occlusion, complex human motion and self-occlusion, and the experimental results show that our method is effective and robust. Number of references: 20 Inspec controlled terms: object tracking - particle filtering (numerical methods) - particle swarm optimisation - pose estimation - simulated annealing - video signal processing Uncontrolled terms: markless human pose tracking - monocular video human motion tracking - particle filter - high dimensional tracking problems - partitioned sampling - 2D human body model - weighing function - likelihood function - occlusion relationship - correlated body limbs - simplified belief hybrid PSO method - video sequences - self-occlusion Inspec classification codes: B6135E Image recognition - B6140B Filtering methods in signal processing - B0260 Optimisation techniques - C5260B Computer vision and image processing techniques - C5260D Video signal processing - C1180 Optimisation techniques Treatment: Practical (PRA); Theoretical or Mathematical (THR) Discipline: Electrical/Electronic engineering (B); Computers/Control engineering (C) DOI: 10.4304/jmm.9.1.106-113 IPC Code: G06K9/00 SECTION G PHYSICS COMPUTING; CALCULATING; COUNTING score computers for games , , ; combinations of writing implements with computing devices RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS postal sorting ; secondary surveillance radar ; detecting presence of transponders or tags , Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints processing or analysis of tracks of nuclear particles ; testing... G06T SECTION G PHYSICS COMPUTING; CALCULATING; COUNTING score computers for games , , ; combinations of writing implements with computing devices IMAGE DATA PROCESSING OR GENERATION, IN GENERAL specially adapted for particular applications, see the relevant subclasses, e.g. , , 6 Database: Inspec |
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