Person Re-identification Based on Feature Fusion and Triplet Loss Function

The task of Person re-identification (re-ID) is to recognize an individual observed by non-overlapping cameras. Robust feature representation is a crucial problem in re-ID. With the rise of deep learning, most current approaches adopt convolutional neural networks (CNN) to extract features. However, the feature representation learned by CNN is often global and lacks detailed local information. To address this issue, this paper proposes a simple CNN architecture consisting of a re-ID subnetwork and an attribute sub-network. In re-ID sub-network, global feature and semantic feature are extracted and fused in a weighted manner, and triplet loss is adopted to further improve the discriminative ability of the learned fusion feature. On the other hand, attribute sub-network focuses on local aspects of a person and offers local structural information that is helpful for re-ID. The two sub-networks are combined on the loss level and their complementary aspects are leveraged to improve the re-ID accuracy. Comparative evaluations demonstrate that our method outperforms several state-of-the-art ones. On the challenging Market1501 and DukeMTMC datasets, 86.3% rank-1 accuracy and 69.4% mAP, and 72.1% rank-1 accuracy and 53.4% mAP are achieved respectively.