利用分数归一化改进基于CNN的行人重识别
Improving CNN-based Person Re-identification using score Normalization
AI总结：
本文提出一种结合CNN特征提取、XQDA度量学习、马氏距离匹配与分数归一化的行人重识别方法，在四个数据集上验证了归一化可有效提升rank-20准确率。
摘要：
行人重识别（PRe-ID）是安全、监控和零售分析中的一项关键任务，涉及在多个摄像头和视角下识别个体。然而，由于光照、背景和视角的变化，这是一项具有挑战性的任务。高效的特征提取和度量学习算法对于成功的PRe-ID系统至关重要。本文提出了一种新的PRe-ID方法，该方法将基于卷积神经网络（CNN）的特征提取方法与用于度量学习的跨视角二次判别分析（XQDA）相结合。此外，还实现了一种匹配算法，该算法采用马氏距离和分数归一化过程来解决摄像头分数之间的不一致问题。所提出的方法在四个具有挑战性的数据集上进行了测试，包括VIPeR、GRID、CUHK01和PRID450S，并取得了有希望的结果。例如，在未归一化的情况下，GRID、CUHK01、VIPeR和PRID450S数据集的rank-20准确率分别为61.92%、83.90%、92.03%、96.22%；然而，经过分数归一化后，它们分别提高到64.64%、89.30%、92.78%和98.76%。因此，在四个具有挑战性的数据集上的有希望的结果表明了所提出方法的有效性。
英文摘要：
Person re-identification (PRe-ID) is a crucial task in security, surveillance, and retail analysis, which involves identifying an individual across multiple cameras and views. However, it is a challenging task due to changes in illumination, background, and viewpoint. Efficient feature extraction and metric learning algorithms are essential for a successful PRe-ID system. This paper proposes a novel approach for PRe-ID, which combines a Convolutional Neural Network (CNN) based feature extraction method with Cross-view Quadratic Discriminant Analysis (XQDA) for metric learning. Additionally, a matching algorithm that employs Mahalanobis distance and a score normalization process to address inconsistencies between camera scores is implemented. The proposed approach is tested on four challenging datasets, including VIPeR, GRID, CUHK01, and PRID450S, and promising results are obtained. For example, without normalization, the rank-20 rate accuracies of the GRID, CUHK01, VIPeR and PRID450S datasets were 61.92%, 83.90%, 92.03%, 96.22%; however, after score normalization, they have increased to 64.64%, 89.30%, 92.78%, and 98.76%, respectively. Accordingly, the promising results on four challenging datasets indicate the effectiveness of the proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
