Implementing Manifold Learning in Adaptive MCMC for Tracking Vehicle under Disturbances

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dc.contributor.authorWei, Yeang Kow
dc.contributor.authorYit, Kwong Chin
dc.contributor.authorWei, Leong Khong
dc.contributor.authorHui, Keng Lau
dc.contributor.authorTze, Kin Teo (Kenneth)
dc.date.accessioned2023-06-06T06:32:46Z
dc.date.available2023-06-06T06:32:46Z
dc.date.issued2012
dc.descriptionModelling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu, Malaysia msclab@ums.edu.my, ktkteo@ieee.or Keywords—Markov Chain Monte Carlo (MCMC), Isometric Feature Mapping (Isomap), variance ratio (VR).
dc.description.abstractIn recent years, tracking vehicle with overlapping and maneuvering disturbances has become a challenging task in visual tracking. Markov Chain Monte Carlo (MCMC) is proved to be effective in tracking vehicle under disturbances by probabilistically estimating the vehicle position. However the sampling based tracking algorithm is highly depending on the sampling efficiencies where adequate chain length is necessary to sustain the tracking accuracy. Therefore variance ratio (VR) based MCMC has been implemented in this study to adapt the chain length according to the disturbances encountered. Isomap manifold learning is further implemented to update the vehicle model and accurately track the vehicle with maneuvering disturbances. Multiple vehicle models with different viewing angles are represented by Isomap under low dimensional manifold. The suitable vehicle model will be selected according to the estimated vehicle position. Experimental results have shown that Isomap-VR-MCMC have better tracking performances compared to VR-MCMC with smaller RMSE value.
dc.identifier.urihttps://digitallibrary.peninsulacollege.edu.my/handle/123456789/329
dc.language.isoen
dc.titleImplementing Manifold Learning in Adaptive MCMC for Tracking Vehicle under Disturbances
dc.typeArticle
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