论文标题
红外彩色人员重新识别的域对抗培训
Domain Adversarial Training for Infrared-colour Person Re-Identification
论文作者
论文摘要
由于它在视频监视中所扮演的角色，人们重新识别（RE-ID）是计算机视觉研究的非常活跃的领域。当前，大多数方法仅解决颜色图像之间匹配的任务。但是，在光线不佳的环境中，CCTV摄像机切换到红外成像，因此开发一个可以在红外和颜色图像之间正确执行匹配的系统是必要的。在本文中，我们提出了一个部分功能提取网络，以更好地关注对红外和颜色方式可见的人的微妙，独特的签名。为了训练模型，我们提出了域对抗特征学习框架的新型变体。通过广泛的实验，我们表明我们的方法优于最先进的方法。
Person re-identification (re-ID) is a very active area of research in computer vision, due to the role it plays in video surveillance. Currently, most methods only address the task of matching between colour images. However, in poorly-lit environments CCTV cameras switch to infrared imaging, hence developing a system which can correctly perform matching between infrared and colour images is a necessity. In this paper, we propose a part-feature extraction network to better focus on subtle, unique signatures on the person which are visible across both infrared and colour modalities. To train the model we propose a novel variant of the domain adversarial feature-learning framework. Through extensive experimentation, we show that our approach outperforms state-of-the-art methods.
