  Various tasks encountered in real-world surveillance can be addressed by
determining posteriors (e.g. by Bayesian inference or machine learning), based
on which critical decisions must be taken. However, the surveillance domain
(acquisition device, operating conditions, etc.) is often unknown, which
prevents any possibility of scene-specific optimization. In this paper, we
define a probabilistic framework and present a formal proof of an algorithm for
the unsupervised many-to-infinity domain adaptation of posteriors. Our proposed
algorithm is applicable when the probability measure associated with the target
domain is a convex combination of the probability measures of the source
domains. It makes use of source models and a domain discriminator model trained
off-line to compute posteriors adapted on the fly to the target domain.
Finally, we show the effectiveness of our algorithm for the task of semantic
segmentation in real-world surveillance. The code is publicly available at
https://github.com/rvandeghen/MDA.
