Abstract
As a research field of symmetry journals, computer vision has received more and more attention. Person re-identification (re-ID) has become a research hotspot in computer vision. We focus on one-example person re-ID, where each person only has one labeled image in the dataset, and other images are unlabeled. There are two main challenges of the task, the insufficient labeled data, and the lack of labeled images cross-cameras. In dealing with the above issue, we propose a new one-example labeling scheme, which generates style-transferred images by CycleGAN (Cycle Generative Adversarial Networks) to ensure that for each person, there is one labeled image under each camera style. Then a self-learning framework is adopted, which iteratively train a CNN (Convolutional Neural Networks) model with labeled images and labeled style-transferred images, and mine the reliable images to assign a pseudo label. The experimental results prove that by integrating the camera style transferred images, we effectively expand the dataset, and the problem of low recognition rate caused by the lack of labeled pedestrian pictures across cameras is effectively solved. Notably, the rank-1 accuracy of our method outperforms the state-of-the-art method by 8.7 points on the Market-1501 dataset, and 6.3 points on the DukeMTMC-ReID dataset.
Highlights
Symmetry is an interdisciplinary comprehensive open-access journal that specializes in studying symmetry in various fields such as physics, chemistry, biology, computers, and mathematics
Person re-identification is a technology that uses computer vision-related technologies to find specific pedestrians of interest from the data gallery taken by multiple surveillance cameras
CycleGAN consists of two discriminators (DA and DB ) and two generators (GAB and GBA ), which can realize the style transformation from both A to B and B to A
Summary
Symmetry is an interdisciplinary comprehensive open-access journal that specializes in studying symmetry in various fields such as physics, chemistry, biology, computers, and mathematics. The specific method is to initialize a CNN model with labeled pictures first, and uses the nearest neighbor (NN) classifier [6] to select the nearest unlabeled data in the feature space of the labeled data, and give them pseudo-labels. Wu et al [9] proposed a progressive learning framework that better utilizes unlabeled data for person re-ID training using limited samples, and dynamically select unlabeled data and assign it a pseudo label by using nearest neighbor (NN) classifier. A camera style transfer model is adopted to use as an augmentation method, which increases the number of labeled data and provided labeled data under more camera style as well With these generated training data, more cross-camera information could be exploited for one-example re-ID.
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