  Object Re-identification (ReID) aims to retrieve the probe object from many
gallery images with the ReID model inferred based on a stationary camera-free
dataset by associating and collecting the identities across all camera views.
When deploying the ReID algorithm in real-world scenarios, the aspect of
storage, privacy constraints, and dynamic changes of cameras would degrade its
generalizability and applicability. Treating each camera's data independently,
we introduce a novel ReID task named Camera-Incremental Object
Re-identification (CIOR) by continually optimizing the ReID mode from the
incoming stream of the camera dataset. Since the identities under different
camera views might describe the same object, associating and distilling the
knowledge of common identities would boost the discrimination and benefit from
alleviating the catastrophic forgetting. In this paper, we propose a novel
Identity Knowledge Evolution (IKE) framework for CIOR, consisting of the
Identity Knowledge Association (IKA), Identity Knowledge Distillation (IKD),
and Identity Knowledge Update (IKU). IKA is proposed to discover the common
identities between the current identity and historical identities. IKD has
applied to distillate historical identity knowledge from common identities and
quickly adapt the historical model to the current camera view. After each
camera has been trained, IKU is applied to continually expand the identity
knowledge by combining the historical and current identity memories. The
evaluation of Market-CL and Veri-CL shows the Identity Knowledge Evolution
(IKE) effectiveness for CIOR.
code:https://github.com/htyao89/Camera-Incremental-Object-ReID
