Labelled Non-Zero Diffusion Particle Flow SMC-PHD Filtering for Multi-Speaker Tracking

Particle flow (PF) is a method originally proposed for single target tracking, and used recently to address the weight degeneracy problem of the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter for audio-visual (AV) multi-speaker tracking, where the particle flow is calculated by using only the measurements near the particle, assuming that the target is detected, as in a recent method based on non-zero particle flow (NPF), i.e. the AV-NPF-SMC-PHD filter. This, however, can be problematic when occlusion happens and the occluded speaker may not be detected. To address this issue, we propose a new method where the labels of the particles are estimated using the likelihood function, and the particle flow is calculated in terms of the selected particles with the same labels. As a result, the particles associated with detected speakers and undetected speakers are distinguished based on the particle labels. With this novel method, named as AV-LPF-SMC-PHD, the speaker states can be estimated as the weighted mean of the labelled particles, which is computationally more efficient than using a clustering method as in the AV-NPF-SMC-PHD filter. The proposed algorithm is compared systematically with several baseline tracking methods using the AV16.3, AVDIAR and CLEAR datasets, and is shown to offer improved tracking accuracy with a lower computational cost.