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Robust object tracking under appearance change conditions
- Source :
- International Journal of Automation and Computing. 7:31-38
- Publication Year :
- 2010
- Publisher :
- Springer Science and Business Media LLC, 2010.
-
Abstract
- We propose a robust visual tracking framework based on particle filter to deal with the object appearance changes due to varying illumination, pose variantions, and occlusions. We mainly improve the observation model and re-sampling process in a particle filter. We use on-line updating appearance model, affine transformation, and M-estimation to construct an adaptive observation model. On-line updating appearance model can adapt to the changes of illumination partially. Affine transformation-based similarity measurement is introduced to tackle pose variantions, and M-estimation is used to handle the occluded object in computing observation likelihood. To take advantage of the most recent observation and produce a suboptimal Gaussian proposal distribution, we incorporate Kalman filter into a particle filter to enhance the performance of the resampling process. To estimate the posterior probability density properly with lower computational complexity, we only employ a single Kalman filter to propagate Gaussian distribution. Experimental results have demonstrated the effectiveness and robustness of the proposed algorithm by tracking visual objects in the recorded video sequences.
- Subjects :
- business.industry
Applied Mathematics
Gaussian
Kalman filter
Computer Science Applications
Active appearance model
symbols.namesake
Control and Systems Engineering
Robustness (computer science)
Modeling and Simulation
Video tracking
symbols
Computer vision
Ensemble Kalman filter
Artificial intelligence
Affine transformation
Particle filter
business
Mathematics
Subjects
Details
- ISSN :
- 17518520 and 14768186
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- International Journal of Automation and Computing
- Accession number :
- edsair.doi...........03e36a00aa5e5356db34a9bdffcf4fdd