Non-Zero Diffusion Particle Flow SMC-PHD Filter for Audio-Visual Multi-Speaker Tracking

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 nonzero 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.

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