Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification

• URL: http://arxiv.org/abs/2203.15210v1
• Date: Tue, 29 Mar 2022 03:10:24 GMT
• Title: Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification
• Authors: Chao Wu, Wenhang Ge, Ancong Wu, Xiaobin Chang
• Abstract summary: Cross-camera images could be unavailable under the ISolated Camera Supervised setting, e.g., a surveillance system deployed across distant scenes. A new pipeline is introduced by synthesizing the cross-camera samples in the feature space for model training. Experiments on two ISCS person Re-ID datasets demonstrate the superiority of our CCSFG to the competitors.
• Score: 24.63519986072777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role. However, such cross-view training samples could be unavailable under the ISolated Camera Supervised (ISCS) setting, e.g., a surveillance system deployed across distant scenes.To handle this challenging problem, a new pipeline is introduced by synthesizing the cross-camera samples in the feature space for model training. Specifically, the feature encoder and generator are end-to-end optimized under a novel method, Camera-Conditioned Stable Feature Generation (CCSFG). Its joint learning procedure raises concern on the stability of generative model training. Therefore, a new feature generator, $\sigma$-Regularized Conditional Variational Autoencoder ($\sigma$-Reg.~CVAE), is proposed with theoretical and experimental analysis on its robustness. Extensive experiments on two ISCS person Re-ID datasets demonstrate the superiority of our CCSFG to the competitors.

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