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Visual tracking in high-dimensional particle filter
- Source :
- PLoS ONE, Vol 13, Iss 8, p e0201872 (2018), PLoS ONE
- Publication Year :
- 2018
- Publisher :
- Public Library of Science (PLoS), 2018.
-
Abstract
- In this paper, we propose a novel object tracking algorithm by using high-dimensional particle filter and combined features. Firstly, the refined two-dimensional principal component analysis and the tendency are combined to represent an object. Secondly, we present a framework using high-order Monte Carlo Markov Chain which considers more information and performs more discriminative and efficient on moving objects than the traditional first-order particle filtering. Finally, an advanced sequential importance resampling is applied to estimate the posterior density and obtains the high-quality particles. To further gain the better samples, K-means clustering is used to select more typical particles, which reduces the computational cost. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the performance of our proposed algorithm is superior to the state-of-the-art methods.
- Subjects :
- Computer science
Density
lcsh:Medicine
02 engineering and technology
Tracking (particle physics)
Pattern Recognition, Automated
Mathematical and Statistical Techniques
Discriminative model
Materials Physics
Resampling
Image Processing, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
Cluster Analysis
lcsh:Science
Principal Component Analysis
Multidisciplinary
Approximation Methods
Applied Mathematics
Simulation and Modeling
Physics
Markov Chains
Monte Carlo method
Physical Sciences
Principal component analysis
symbols
020201 artificial intelligence & image processing
Particle filter
Algorithms
Statistics (Mathematics)
Research Article
Markov Models
Materials Science
Material Properties
Research and Analysis Methods
Motion
symbols.namesake
Statistical Methods
Cluster analysis
business.industry
lcsh:R
Bayes Theorem
020206 networking & telecommunications
Markov chain Monte Carlo
Pattern recognition
Probability Theory
Probability Distribution
Probability Density
Multivariate Analysis
Eye tracking
lcsh:Q
Artificial intelligence
business
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....5f4a34d2c09a98ebfc71d5abcd8048f7