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

Camera-Incremental Object Re-Identification with Identity Knowledge Evolution