Non-Zero Diffusion Particle Flow SMC-PHD Filter for Audio-Visual Multi-Speaker Tracking
Yang Liu, Adrian Hilton, Jonathon A. Chambers, Yuxin Zhao, Wenwu Wang
Abstract
The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been shown to be promising for audio-visual multi-speaker tracking. Recently, the zero diffusion particle flow (ZPF) has been used to mitigate the weight degeneracy problem in the SMC-PHD filter. However, this leads to a substantial increase in the computational cost due to the migration of particles from prior to posterior distribution with a partial differential equation. This paper proposes an alternative method based on the non-zero diffusion particle flow (NPF) to adjust the particle states by fitting the particle distribution with the posterior probability density using the non-zero diffusion. This property allows efficient computation of the migration of particles. Results from the AV16.3 dataset demonstrate that we can significantly mitigate the weight degeneracy problem with a smaller computational cost as compared with the ZPF based SMC-PHD filter.
BibTeX
@inproceedings{icassp2018_nonzerodiffusion,
title = {Non-Zero Diffusion Particle Flow SMC-PHD Filter for Audio-Visual Multi-Speaker Tracking},
author = {Yang Liu and Adrian Hilton and Jonathon A. Chambers and Yuxin Zhao and Wenwu Wang},
booktitle = {ICASSP 2018},
year = {2018}
}