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Labelled non-zero particle flow for SMC-PHD filtering

Liu, Yang, Hu, Qinghua, Zou, Yuexian and Wang, Wenwu (2019) Labelled non-zero particle flow for SMC-PHD filtering In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12 - 17 May 2019, Brighton, UK.

2018ICASSP (4).pdf - Accepted version Manuscript

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The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter assisted by particle flows (PF) has been shown to be promising for audio-visual multi-speaker tracking. A clustering step is often employed for calculating the particle flow, which leads to a substantial increase in the computational cost. To address this issue, we propose an alternative method based on the labelled non-zero particle flow (LNPF) to adjust the particle states. Results obtained from the AV16.3 dataset show improved performance by the proposed method in terms of computational efficiency and tracking accuracy as compared with baseline AV-NPF-SMC-PHD methods.

Item Type: Conference or Workshop Item (Conference Poster)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Hu, Qinghua
Zou, Yuexian
Date : 1 February 2019
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Audio-visual Tracking, SMC-PHD Filter, Particle Flow
Depositing User : Charlene King
Date Deposited : 01 Mar 2019 10:11
Last Modified : 12 May 2019 02:08

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