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- W2373073731 abstract "To resolve the target-tracking in coal mines,which was caused by a single-cue lacking of discrimination to target features and the reliability of observation modals lacking of self-adaptation to changes of scenes,a novel unscented particle filter(UPF) algorithm was proposed for object-tracking using an unscented kalman filter(UKF)as proposal distribution and an adaptive multi-cues fusion modal as the observation modal.To integrate the latest observation into proposal distribution,the UKF generate prediction samples were adjusted to high likelihood area in state-space,and more overlap regions of prediction samples and peak zones of observation likelihood were achieved.The optical flow histogram was presented and observation model based on multi-cues fusion was implemented by integrating optical flow with color.The adaptive strategy of observation modal weights was implemented by adjusting the contribution rate of single-cue observation modal,and the reliability of an observation modal was adjusted.A function of sample compensation was proposed to handle particle diffusion due to failure of observation modal.The results show that the tracking algorithm is effective for solving tracking failure in coal mines(complex background).The estimating accuracy was increased by 87% compared with three other particle filter algorithms." @default.
- W2373073731 created "2016-06-24" @default.
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- W2373073731 date "2011-01-01" @default.
- W2373073731 modified "2023-09-26" @default.
- W2373073731 title "Unscented particle filter algorithm for people tracking in coal mines based on adaptive multi-cues integration models" @default.
- W2373073731 hasPublicationYear "2011" @default.
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