  Person search is the task to localize a query person in gallery datasets of
scene images. Existing methods have been mainly developed to handle a single
target dataset only, however diverse datasets are continuously given in
practical applications of person search. In such cases, they suffer from the
catastrophic knowledge forgetting in the old datasets when trained on new
datasets. In this paper, we first introduce a novel problem of lifelong person
search (LPS) where the model is incrementally trained on the new datasets while
preserving the knowledge learned in the old datasets. We propose an end-to-end
LPS framework that facilitates the knowledge distillation to enforce the
consistency learning between the old and new models by utilizing the prototype
features of the foreground persons as well as the hard background proposals in
the old domains. Moreover, we also devise the rehearsal-based instance matching
to further improve the discrimination ability in the old domains by using the
unlabeled person instances additionally. Experimental results demonstrate that
the proposed method achieves significantly superior performance of both the
detection and re-identification to preserve the knowledge learned in the old
domains compared with the existing methods.
