大数据环境下基于深度学习的行人再识别

针对卷积神经网络在行人识别过程中错误率较高的问题，提出了一种基于深度胶囊模型的行人再识别方法.首先利用标准卷积层学习区分度较高的特征；然后将不同卷积层中的若干特征划分为一组，生成一个具有丰富语义特征的主胶囊.在此基础上，引入了动态路由算法，通过迭代路由过程来确定主胶囊和数字胶囊之间的归属关系，进而得到一组数字胶囊，其中，每个数字胶囊可以学习识别目标行人的存在.在具有挑战性的数据集上进行实验的结果表明，所提算法在性能上优于已有算法.

Research on Person Re-Identification Based on Deep Learning under Big Data Environment

Convolutional neural networks produce higher probability of error for person re-identifications. To overcome the shortcomings, a new deep learning method based on capsule networks model for person re-identification was proposed. First, the standard convolutional layers are used to learn discriminative features. Then, several features in different layers are grouped together to form the primary capsules which represent a rich semantic features. After that, a dynamic routing algorithm which is an iterative routing process, is introduced to decide the attribution between primary capsule and digital capsule. To this end, the digital capsule layer is obtained and each capsule can learn to recognize the presence of persons. To highlight the superiorities of the proposed algorithm, extensive experiments are conducted on a series of challenging datasets and show that the algorithm favorably performs against the previous work.