关键词: 行人再识别, 卷积神经网络, 胶囊网络, 主胶囊, 数字胶囊

Abstract: 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.

Key words: person re-identification, convolutional neural networks, capsule networks, primary capsule, digital capsule