  Audio-visual speaker tracking has drawn increasing attention over the past
few years due to its academic values and wide application. Audio and visual
modalities can provide complementary information for localization and tracking.
With audio and visual information, the Bayesian-based filter can solve the
problem of data association, audio-visual fusion and track management. In this
paper, we conduct a comprehensive overview of audio-visual speaker tracking. To
our knowledge, this is the first extensive survey over the past five years. We
introduce the family of Bayesian filters and summarize the methods for
obtaining audio-visual measurements. In addition, the existing trackers and
their performance on AV16.3 dataset are summarized. In the past few years, deep
learning techniques have thrived, which also boosts the development of audio
visual speaker tracking. The influence of deep learning techniques in terms of
measurement extraction and state estimation is also discussed. At last, we
discuss the connections between audio-visual speaker tracking and other areas
such as speech separation and distributed speaker tracking.

Multimedia;Sound;Audio and Speech Processing