  Person search is an extended task of person re-identification (Re-ID).
However, most existing one-step person search works have not studied how to
employ existing advanced Re-ID models to boost the one-step person search
performance due to the integration of person detection and Re-ID. To address
this issue, we propose a faster and stronger one-step person search framework,
the Teacher-guided Disentangling Networks (TDN), to make the one-step person
search enjoy the merits of the existing Re-ID researches. The proposed TDN can
significantly boost the person search performance by transferring the advanced
person Re-ID knowledge to the person search model. In the proposed TDN, for
better knowledge transfer from the Re-ID teacher model to the one-step person
search model, we design a strong one-step person search base framework by
partially disentangling the two subtasks. Besides, we propose a Knowledge
Transfer Bridge module to bridge the scale gap caused by different input
formats between the Re-ID model and one-step person search model. During
testing, we further propose the Ranking with Context Persons strategy to
exploit the context information in panoramic images for better retrieval.
Experiments on two public person search datasets demonstrate the favorable
performance of the proposed method.
