Camera-Incremental Object Re-Identification With Identity Knowledge Evolution

Abstract

Object Re-identification (ReID) is a task focused on retrieving a probe object from a multitude of gallery images using a ReID model trained on a stationary, camera-free dataset. This training involves associating and aggregating identities across various camera views. However, when deploying ReID algorithms in real-world scenarios, several challenges, such as storage constraints, privacy considerations, and dynamic changes in camera setups, can hinder their generalizability and practicality. To address these challenges, we introduce a novel ReID task called Camera-Incremental Object Re-identification (CIOR). In CIOR, we treat each camera's data as a separate source and continually optimize the ReID model as new data streams come from various cameras. By associating and consolidating the knowledge of common identities, our aim is to enhance discrimination capabilities and mitigate the problem of catastrophic forgetting. Therefore, we propose a novel Identity Knowledge Evolution (IKE) framework for CIOR, consisting of Identity Knowledge Association (IKA), Identity Knowledge Distillation (IKD), and Identity Knowledge Update (IKU). IKA is proposed to discover common identities between the current identity and historical identities, facilitating the integration of previously acquired knowledge. IKD involves distilling historical identity knowledge from common identities, enabling rapid adaptation of the historical model to the current camera view. After each camera has been trained, IKU is applied to continually expand identity knowledge by combining historical and current identity memories. Market-CL and Veri-CL evaluations show the effectiveness of Identity Knowledge Evolution (IKE) for CIOR.Code: https://github.com/htyao89/Camera-Incremental-Object-ReID