Enhancement of Particle Filter Resampling in Vehicle Tracking via Genetic Algorithm

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dc.contributor.authorWei Leong Khong
dc.contributor.authorWei Yeang Kow
dc.contributor.authorYit Kwong Chin
dc.contributor.authorMei Yeen Choong
dc.contributor.authorKenneth Tze Kin Teo
dc.date.accessioned2023-05-30T07:49:30Z
dc.date.available2023-05-30T07:49:30Z
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.org
dc.description.abstractVehicle tracking is an essential approach that can help to improve the traffic surveillance or assist the road traffic control. Recently, the development of video surveillance infrastructure has incited the researchers to focus on the vehicle tracking by using video sensors. However, the amount of the on-road vehicle has been increased dramatically and hence the congestion of the traffic has made the occlusion scene become a challenge task for video sensor based tracking. Conventional particle filter will encounter tracking error during and after occlusion. Besides that, it also required more iteration to continuously track the vehicle after occlusion. Thus, particle filter with genetic operator resampling has been proposed as the tracking algorithm to faster converge and keep track on the target vehicle under various occlusion incidents. The experimental results show that enhancement of the particle filter with genetic algorithm manage to reduce the particle sample size.
dc.identifier.urihttps://digitallibrary.peninsulacollege.edu.my/handle/123456789/324
dc.language.isoen
dc.publisherUKSim-AMSS 6th European Modelling Symposium
dc.subjectTECHNOLOGY::Engineering mechanics::Vehicle engineering
dc.titleEnhancement of Particle Filter Resampling in Vehicle Tracking via Genetic Algorithm
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