基于深度学习和度量学习的行人再识别

Deep Learning and Metric Learning Based Person Re-identification

跨摄像机行人因光照、视角、姿态的差异,会使其外观变化显著,给行人再识别的研究带来严峻挑战。文中提出基于深度学习和度量学习的行人再识别方法。首先采用手工特征和深度特征融合网络FFN提取行人图像特征,然后将核矩阵应用于KISSME距离度量学习中,获取更优的距离度量模型。在具有挑战的VIPeR和PRID450S两个公开数据集上进行仿真实验,实验结果表明所提出的行人再识别算法的有效性。

Pedestrian may vary greatly in appearance due to differences in illumination,viewpoint,and poses across cameras,which can bring serious challenges in person re-identification.A deep learning and metric learning based algorithm is proposed for person re-identification in this paper.Features of human images are first extracted by a feature fusion net(FFN)composed of handcraft features and deep features,and then a kernel matrix is applied to KISSME distance metric learning to obtain a better distance metric model.Experimental results have shown that the proposed algorithm effectively improves recognition rates on two challenging datasets(VIPeR,PRID450s).