  In this paper, we present an unsupervised probabilistic model and associated
estimation algorithm for multi-object tracking (MOT) based on a dynamical
variational autoencoder (DVAE), called DVAE-UMOT. The DVAE is a latent-variable
deep generative model that can be seen as an extension of the variational
autoencoder for the modeling of temporal sequences. It is included in DVAE-UMOT
to model the objects' dynamics, after being pre-trained on an unlabeled
synthetic dataset of single-object trajectories. Then the distributions and
parameters of DVAE-UMOT are estimated on each multi-object sequence to track
using the principles of variational inference: Definition of an approximate
posterior distribution of the latent variables and maximization of the
corresponding evidence lower bound of the data likehood function. DVAE-UMOT is
shown experimentally to compete well with and even surpass the performance of
two state-of-the-art probabilistic MOT models. Code and data are publicly
available.
