Convolutional Feature Transfer via Camera-Specific Discriminative Pooling for Person Re-Identification
Convolutional Feature Transfer via Camera-Specific Discriminative Pooling for Person Re-Identification
复制标题
DOI：
10.1109/icpr48806.2021.9412420
复制
发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Tetsu Matsukawa;Einoshin Suzuki
中科院分区：
文献类型：
--
作者：
Tetsu Matsukawa;Einoshin Suzuki
现代卷积神经网络（CNN）一直在使用大量训练样本来提高人员重新识别（re-id）的准确性。这种re-id系统缺乏用于部署到实际安全应用的训练样本。为了解决这个问题，我们专注于将在大规模人员re-id数据集上预训练的CNN特征转移到小规模数据集的方法。大多数现有的CNN特征转移方法使用完全连接层的特征，这些特征纠缠图像上不同空间位置的局部池特征。不幸的是，由于视角的差异和人的行走方向的偏差，数据集中的每个相机视图在人图像中具有唯一的空间属性，这降低了针对不同相机/数据集的局部池化的通用性。为了解释相机和相机特定的空间偏差，我们提出了一种方法来学习相机和相机特定的位置权重图，用于卷积特征的区分性局部池化。我们在四个公共数据集上的实验证实了所提出的特征转移的有效性，目标数据集中有少量的训练样本。
Modern Convolutional Neural Networks (CNNs) have been improving the accuracy of person re-identification (re-id) using a large number of training samples. Such a re-id system suffers from a lack of training samples for deployment to practical security applications. To address this problem, we focus on the approach that transfers features of a CNN pre-trained on a large-scale person re-id dataset to a small-scale dataset. Most of the existing CNN feature transfer methods use the features of fully connected layers that entangle locally pooled features of different spatial locations on an image. Unfortunately, due to the difference of view angles and the bias of walking directions of the persons, each camera view in a dataset has a unique spatial property in the person image, which reduces the generality of the local pooling for different cameras/datasets. To account for the camera- and dataset-specific spatial bias, we propose a method to learn camera and dataset-specific position weight maps for discriminative local pooling of convolutional features. Our experiments on four public datasets confirm the effectiveness of the proposed feature transfer with a small number of training samples in the target datasets.