Unsupervised Domain Adaption based on metric learning for Person Re-Identification

Abstract

Person re-identification(ReID) with deep convolutional neural networks(CNNs) has attracted increasing interest in computer vision due to its wide potential applications in visual surveillance and has achieved high performance in recent years using a lot of techniques to overcome the challenges such as variations in view angle, lighting, image occlusion. Another main challenge in person re-identification(ReID) is the cross domain adaptation. Due to different domains, a person re-identification model trained on one dataset with good performance often fails to achieve same or better performance on other datasets. We propose a method which is about both the source and target datasets. We fine-tune the deep CNN model on the labeled source dataset in a supervised manner by using distance metric learning and the unlabeled target dataset in an unsupervised manner simultaneously.