TY -的A2 -汗,爱迪盟-杨,赵盟——刘公司AU -刘,领带AU -王,李盟——赵赛PY - 2020 DA - 2020/12/29 TI -为圆形比损失人Reidentification SP - 9860562六世- 2020 AB -人Reidentification (re-id)的目的是识别一个特定的行人从交叉监控摄像头的观点。大多数re-id方法执行检索任务通过比较相似的行人特征提取深度学习模型。因此,学习一种区别的特性对人reidentification至关重要。许多工作监督学习模型与一个或多个损失函数来获得特性的辨别力。Softmax损失是一种广泛应用在re-id丧失功能。然而,传统softmax损失本身侧重于特征可分性和不考虑在类特性的密实度。进一步提高re-id的准确性,许多努力进行收缩在课堂差异以及类间相似性。在本文中,我们提出一个鉴定为圆形比损失的人。具体地说,我们正常学习特性和分类权重向量映射的超球面。然后我们把最大组内距离的比值和阶级之间的最小距离客观损失,因此类间可分性和在类密实度可以优化同时在训练阶段。 Finally, with the joint training of an improved softmax loss and the ratio loss, the deep model could mine discriminative pedestrian information and learn robust features for the re-id task. Comprehensive experiments on three re-id benchmark datasets are carried out to illustrate the effectiveness of the proposed method. Specially, 83.12% mAP on Market-1501, 71.66% mAP on DukeMTMC-reID, and 66.26%/63.24% mAP on CUHK03 labeled/detected are achieved, respectively. SN - 1076-2787 UR - https://doi.org/10.1155/2020/9860562 DO - 10.1155/2020/9860562 JF - Complexity PB - Hindawi KW - ER -