• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Learning deep features with adaptive triplet loss for person reidentification
【24h】

Learning deep features with adaptive triplet loss for person reidentification

机译：通过自适应三重态损失学习深度特征以进行人识别

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Person reidentification (re-id) aims to match a specified person across non-overlapping cameras, which remains a very challenging problem. While previous methods mostly focus on feature extraction or metric learning, this paper makes the attempt in jointly learning both the global full-body and local body-parts features of the input persons with a multichannel convolutional neural network (CNN) model, which is trained by an adaptive triplet loss function that serves to minimize the distance between the same person and maximize the distance between different persons. The experimental results show that our approach achieves very promising results on the large-scale Market-1501 and DukeMTMC-relD datasets.
机译：人员重新识别（re-id）旨在在不重叠的摄像机之间匹配指定的人员，这仍然是一个非常具有挑战性的问题。尽管先前的方法主要关注特征提取或度量学习，但本文尝试通过多通道卷积神经网络（CNN）模型来联合学习输入人员的全局全身和局部身体部位特征。通过自适应三重态损失函数，该函数用于最小化同一个人之间的距离并最大化不同个人之间的距离。实验结果表明，我们的方法在大规模的Market-1501和DukeMTMC-relD数据集上取得了非常有希望的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号