多属性融合网络的行人重识别方法

针对基于视频的行人重识别中由于光照与视角变化带来的问题,提出了一种结合局域质量评估网络与行人属性特征的网络。对部分行人图像进行预处理,裁掉部分行人图像的底部;将行人分割成三段通过卷积神经网络对其进行质量评估;结合事先人工标注的行人属性标签,进行训练从而完成重识别的过程。通过学习行人的全局特征和局部特征,能够有效解决行人图像中出现的遮挡和不对齐问题,通过在三个数据集上的结果对比表明方法实现了准确率上的提升。

Video-Based Person Re-identification by Attributes Fusion Network

To solve the difficulties brought by illumination and viewpoint varieties in video-based person re-identification(re-ID),a network combining the region-based quality estimation and attribute classification is proposed.Some images are pre-processed by cutting the bottom part.the images are input into convolutional network after being divided into three-part-division,which will predict the quality of each part.the network is trained by combining with the attribute labels of pedestrians to finish the whole re-ID process.Through learning global features and local features of pedestrians,the network can effectively handle occlusions and misalignments,thus achieve comparable results in three public datasets.