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• W2588791810 abstract "Sequential Monte Carlo probability hypothesis density (SMC-PHD) filtering has been recently exploited for audio-visual (AV) based tracking of multiple speakers, where audio data are used to inform the particle distribution and propagation in the visual SMC-PHD filter. However, the performance of the AV-SMC-PHD filter can be affected by the mismatch between the proposal and the posterior distribution. In this paper, we present a new method to improve the particle distribution where audio information (i.e. DOA angles derived from microphone array measurements) is used to detect new born particles and visual information (i.e. histograms) is used to modify the particles with particle flow (PF). Using particle flow has the benefit of migrating particles smoothly from the prior to the posterior distribution. We compare the proposed algorithm with the baseline AV-SMC-PHD algorithm using experiments on the AV16.3 dataset with multi-speaker sequences." @default.
• W2588791810 created "2017-02-24" @default.
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• W2588791810 date "2017-01-01" @default.
• W2588791810 modified "2023-09-23" @default.
• W2588791810 title "Particle Flow SMC-PHD Filter for Audio-Visual Multi-speaker Tracking" @default.
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• W2588791810 doi "https://doi.org/10.1007/978-3-319-53547-0_33" @default.
• W2588791810 hasPublicationYear "2017" @default.
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