  Recent advances in unsupervised domain adaptation have significantly improved
the recognition accuracy of CNNs by alleviating the domain shift between
(labeled) source and (unlabeled) target data distributions. While the problem
of single-target domain adaptation (STDA) for object detection has recently
received much attention, multi-target domain adaptation (MTDA) remains largely
unexplored, despite its practical relevance in several real-world applications,
such as multi-camera video surveillance. Compared to the STDA problem that may
involve large domain shifts between complex source and target distributions,
MTDA faces additional challenges, most notably the computational requirements
and catastrophic forgetting of previously-learned targets, which can depend on
the order of target adaptations. STDA for detection can be applied to MTDA by
adapting one model per target, or one common model with a mixture of data from
target domains. However, these approaches are either costly or inaccurate. The
only state-of-art MTDA method specialized for detection learns targets
incrementally, one target at a time, and mitigates the loss of knowledge by
using a duplicated detection model for knowledge distillation, which is
computationally expensive and does not scale well to many domains. In this
paper, we introduce an efficient approach for incremental learning that
generalizes well to multiple target domains. Our MTDA approach is more suitable
for real-world applications since it allows updating the detection model
incrementally, without storing data from previous-learned target domains, nor
retraining when a new target domain becomes available. Our proposed method,
MTDA-DTM, achieved the highest level of detection accuracy compared against
state-of-the-art approaches on several MTDA detection benchmarks and Wildtrack,
a benchmark for multi-camera pedestrian detection.
