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

## TL;DR

This paper introduces a deep embedding method for person re-identification that adaptively learns local sample distributions, improving robustness to pose, illumination, and occlusion variations, and achieves state-of-the-art results.

## Contribution

It proposes a novel local positive mining strategy and an adaptive similarity metric learning approach for more robust deep feature embedding in person re-id.

## Key findings

- Achieves state-of-the-art performance on benchmark datasets.
- Effectively handles large intra-class variations.
- Improves robustness of feature embedding to visual variations.

## Abstract

Person re-identification (re-id) aims to match pedestrians observed by disjoint camera views. It attracts increasing attention in computer vision due to its importance to surveillance system. To combat the major challenge of cross-view visual variations, deep embedding approaches are proposed by learning a compact feature space from images such that the Euclidean distances correspond to their cross-view similarity metric. However, the global Euclidean distance cannot faithfully characterize the ideal similarity in a complex visual feature space because features of pedestrian images exhibit unknown distributions due to large variations in poses, illumination and occlusion. Moreover, intra-personal training samples within a local range are robust to guide deep embedding against uncontrolled variations, which however, cannot be captured by a global Euclidean distance. In this paper, we study the problem of person re-id by proposing a novel sampling to mine suitable \textit{positives} (i.e. intra-class) within a local range to improve the deep embedding in the context of large intra-class variations. Our method is capable of learning a deep similarity metric adaptive to local sample structure by minimizing each sample's local distances while propagating through the relationship between samples to attain the whole intra-class minimization. To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding. This yields local discriminations by selecting local-ranged positive samples, and the learned features are robust to dramatic intra-class variations. Experiments on benchmarks show state-of-the-art results achieved by our method.