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Particle ow SMC-PHD lter for audio-visual multi-speaker tracking

Liu, Yang, Wang, Wenwu, Chambers, J, Kilic, V and Hilton, Adrian (2017) Particle ow SMC-PHD lter for audio-visual multi-speaker tracking In: 13th International Conference on Latent Variable Analysis and Signal Separation, 2017-02-21 - 2017-02-23, Grenoble, France.

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Sequential Monte Carlo probability hypothesis density (SMC- PHD) ltering 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 lter. How- ever, the performance of the AV-SMC-PHD lter can be a ected by the mismatch between the proposal and the posterior distribution. In this pa- per, we present a new method to improve the particle distribution where audio information (i.e. DOA angles derived from microphone array mea- surements) is used to detect new born particles and visual information (i.e. histograms) is used to modify the particles with particle ow (PF). Using particle ow has the bene t of migrating particles smoothly from the prior to the posterior distribution. We compare the proposed algo- rithm with the baseline AV-SMC-PHD algorithm using experiments on the AV16.3 dataset with multi-speaker sequences.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Chambers, J
Kilic, V
Date : 15 February 2017
DOI : 10.1007/978-3-319-53547-0_33
Copyright Disclaimer : The final publication is available at
Uncontrolled Keywords : Audio-visual tracking, PHD lter, SMC implementation, multi-speaker tracking
Related URLs :
Additional Information : Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)
Depositing User : Symplectic Elements
Date Deposited : 14 Feb 2017 18:17
Last Modified : 16 Jan 2019 17:12

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