# Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification Source: [Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification]() Collection index: [llms.txt]() Data updated: 2026-01-04T05:12:24.152Z Authors: Chao Wu, Wenhang Ge, Ancong Wu, Xiaobin Chang Published: 2022-03-29T03:10:24.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Person Re-identification on Market-SCT](): Person Re-Identification; Market-SCT; Rank-1 Accuracy (R-1): 87.1. - [Person Re-identification on MSMT-SCT](): Person Re-Identification; MSMT-SCT; R-1: 61.2. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()