Embedding Deep Metric for Person Re-identication A Study Against Large Variations

by   Hailin Shi, et al.

Person re-identification is challenging due to the large variations of pose, illumination, occlusion and camera view. Owing to these variations, the pedestrian data is distributed as highly-curved manifolds in the feature space, despite the current convolutional neural networks (CNN)'s capability of feature extraction. However, the distribution is unknown, so it is difficult to use the geodesic distance when comparing two samples. In practice, the current deep embedding methods use the Euclidean distance for the training and test. On the other hand, the manifold learning methods suggest to use the Euclidean distance in the local range, combining with the graphical relationship between samples, for approximating the geodesic distance. From this point of view, selecting suitable positive i.e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations. In this paper, we propose a novel moderate positive sample mining method to train robust CNN for person re-identification, dealing with the problem of large variation. In addition, we improve the learning by a metric weight constraint, so that the learned metric has a better generalization ability. Experiments show that these two strategies are effective in learning robust deep metrics for person re-identification, and accordingly our deep model significantly outperforms the state-of-the-art methods on several benchmarks of person re-identification. Therefore, the study presented in this paper may be useful in inspiring new designs of deep models for person re-identification.


page 4

page 10

page 13


Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification

Person re-identification (re-id) aims to match pedestrians observed by d...

Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification

Person re-identification (Re-ID) aims at matching images of the same per...

Person Re-Identification by Semantic Region Representation and Topology Constraint

Person re-identification is a popular research topic which aims at match...

SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification

Many current successful Person Re-Identification(ReID) methods train a m...

Spectral Feature Transformation for Person Re-identification

With the surge of deep learning techniques, the field of person re-ident...

Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions

Person re-identification (ReID) under occlusions is a challenging proble...

Support Neighbor Loss for Person Re-Identification

Person re-identification (re-ID) has recently been tremendously boosted ...

Please sign up or login with your details

Forgot password? Click here to reset