计算机科学
规范化(社会学)
人工智能
卷积神经网络
模式识别(心理学)
特征提取
马氏距离
线性判别分析
公制(单位)
机器学习
运营管理
社会学
人类学
经济
作者
Ammar Chouchane,Abdelmalik Ouamane,Yassine Himeur,Wathiq Mansoor,Shadi Atalla,Afaf Benzaibak,Chahrazed Boudellal
标识
DOI:10.1109/icip49359.2023.10222062
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. The proposed approach has demonstrated its effectiveness through promising results obtained from the four challenging datasets.
科研通智能强力驱动
Strongly Powered by AbleSci AI