Multi-cue fusion: Discriminative enhancing for person re-identification

Abstract

Person re-identification is an emerging research field in computer vision. Our paper aims to study how to improve the discrimination of person features. We find that some peculiarities of people have not been better attention in the semantic features of deep learning. However, some features obtained by traditional methods can better express the color, and these features are an important clue for re-identification. Therefore, in this paper, we combine traditional Gaussian features with deep semantic features to enhance the discrimination of overall features. At last, we have achieved good performance on two public datasets (Market1501 and VIPeR) in three main distance method learning (DML). In addition, we applied this model to the task of vehicle re-identification. Experiments show that our method has a great improvement on the VeRi vehicle dataset. We compare the results with the current high level results, which indicates the effectiveness of our model.