Person Re-Identification with Hybrid Loss and Hard Triplets Mining

Abstract

Person re-identification is the process of recognizing a person through a network of cameras. Recently, many models of person re-identification based on deep learning have been proposed. In these models, the choice of loss function is vital, since different loss function has different characteristics. Cross-entropy and triplet losses are two commonly used loss functions. Unfortunately, triplet loss cannot measure the overall spatial distribution of features, while the cross-entropy loss does not have enough discriminant between features. In this paper, we propose a new hybrid loss function to learn a better spatial distribution of features and distance between features. Furthermore, we design a strategy to mine hard triplets to accelerate the learning. Experimental results demonstrate that the proposed method is effective and improves the accuracy of person re-identification when compared with the state-of-the-art.