论文标题
相机条件的稳定功能生成，用于孤立的相机监督人员重新识别
Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification
论文作者
论文摘要
为了学习人重新识别的相机视图不变功能（RE-ID），每个人的跨相机图像对起着重要作用。但是，在孤立的相机监督（ISCS）设置中，例如，在遥远场景中部署的监视系统中，这种跨视图训练样本可能无法使用。为了解决这个具有挑战性的问题，通过合成用于模型训练的功能空间中的跨摄像机样本来引入新的管道。具体而言，特征编码器和发电机是根据新颖的方法，摄像头的稳定特征生成（CCSFG）进行了优化的端到端。它的联合学习程序引起了人们对生成模型培训的稳定性的关注。因此，提出了一种新功能生成器，即$σ$的条件变异自动编码器（$σ$ -REG。〜CVAE），对其稳健性进行了理论和实验分析。对两个ISC人Re-ID数据集进行的广泛实验证明了我们CCSFG对竞争对手的优越性。
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, $σ$-Regularized Conditional Variational Autoencoder ($σ$-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.
