Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identification

• URL: http://arxiv.org/abs/2311.01155v1
• Date: Thu, 2 Nov 2023 11:32:40 GMT
• Title: Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identification
• Authors: Menglin Wang, Xiaojin Gong
• Abstract summary: Cross-camera images are prone to being recognized as different IDs simply by camera style. This paper studies person re-ID under such isolated camera supervised (ISCS) setting.
• Score: 6.477096324232456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supervised person re-identification assumes that a person has images captured under multiple cameras. However when cameras are placed in distance, a person rarely appears in more than one camera. This paper thus studies person re-ID under such isolated camera supervised (ISCS) setting. Instead of trying to generate fake cross-camera features like previous methods, we explore a novel perspective by making efficient use of the variation in training data. Under ISCS setting, a person only has limited images from a single camera, so the camera bias becomes a critical issue confounding ID discrimination. Cross-camera images are prone to being recognized as different IDs simply by camera style. To eliminate the confounding effect of camera bias, we propose to learn both intra- and inter-camera invariance under a unified framework. First, we construct style-consistent environments via clustering, and perform prototypical contrastive learning within each environment. Meanwhile, strongly augmented images are contrasted with original prototypes to enforce intra-camera augmentation invariance. For inter-camera invariance, we further design a much improved variant of multi-camera negative loss that optimizes the distance of multi-level negatives. The resulting model learns to be invariant to both subtle and severe style variation within and cross-camera. On multiple benchmarks, we conduct extensive experiments and validate the effectiveness and superiority of the proposed method. Code will be available at https://github.com/Terminator8758/IICI.

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