Abstract-The probability hypothesis density (PHD) filter based on sequential Monte Carlo (SMC) approximation (also known as SMC-PHD filter) has proven to be a promising algorithm for multispeaker tracking. However, it has a heavy computational cost as surviving, spawned, and born particles need to be distributed in each frame to model the state of the speakers and to estimate jointly the variable number of speakers with their states. In particular, the computational cost is mostly caused by the born particles as they need to be propagated over the entire image in every frame to detect the new speaker presence in the view of the visual tracker. In this paper, we propose to use the audio data to improve the visual SMC-PHD (V-SMC-PHD) filter by using the direction of arrival angles of the audio sources to determine when to propagate the born particles and reallocate the surviving and spawned particles. The tracking accuracy of the audio-visual SMC-PHD (AV-SMC-PHD) algorithm is further improved by using a modified mean-shift algorithm to search and climb density gradients iteratively to find the peak of the probability distribution, and the extra computational complexity introduced by mean-shift is controlled with a sparse sampling technique. These improved algorithms, named as AVMS-SMC-PHD and sparse-AVMS-SMC-PHD, respectively, are compared systematically with AV-SMC-PHD and V-SMC-PHD based on the AV16.3, AMI, and CLEAR datasets. Index Terms-Audio-visual tracking, mean-shift, multi-speaker tracking, probability hypothesis density (PHD) filter, sequential Monte Carlo (SMC) implementation, sparse particles.
In REF DOAs of audio sources to guide the propagation of particles and combined the filter with a mean-shift algorithm to reduce the computational complexity.
Mean-Shift and Sparse Sampling-Based SMC-PHD Filtering for Audio Informed Visual Speaker Tracking
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Computer Science