Abstract: The person re-identification refers to matching pedestrian images observed from different cameras in a non-overlapping multi-camera surveillance systems. In this article, a person re-identification method based on transfer learning is proposed. The deep convolutional neural network model pre-trained on ImageNet is adopted to fine-tune the parameters for person re-identification, and the Re-ID model is used to extract the features from person image. The simple cosine distance is applied to measure similarities between person pairs. The approach is evaluated by operating in-depth experiments in three benchmarks, including CUHK03, Market-1501 and DukeMTMC-reID, and the experimental results on the benchmarks show significant and consistent improvements over parallel methods.

Key words: person re-identification, deep convolutional neural network, Transfer learning