  Random Forest (RF) is a successful paradigm for learning classifiers due to
its ability to learn from large feature spaces and seamlessly integrate
multi-class classification, as well as the achieved accuracy and processing
efficiency. However, as many other classifiers, RF requires domain adaptation
(DA) provided that there is a mismatch between the training (source) and
testing (target) domains which provokes classification degradation.
Consequently, different RF-DA methods have been proposed, which not only
require target-domain samples but revisiting the source-domain ones, too. As
novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt
and Tree-Adapt) that only require the learned source-domain RF and a relatively
few target-domain samples for DA, i.e. source-domain samples do not need to be
available. To assess the performance of our proposals we focus on image-based
object detection, using the pedestrian detection problem as challenging
proof-of-concept. Moreover, we use the RF with expert nodes because it is a
competitive patch-based pedestrian model. We test our Node-, Path- and
Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.
