Lifelong Person Search

Jae-Won Yang, Seungbin Hong, Jae-Young Sim

Abstract:

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.