Person Re-identification by Saliency Learning

TL;DR

This paper introduces a novel person re-identification method that leverages learned human saliency to improve matching accuracy across different camera views, addressing challenges like viewpoint and pose variations.

Contribution

It proposes a saliency learning and matching framework that estimates distinctive features without identity labels and integrates saliency with patch matching in a RankSVM model.

Findings

Outperforms state-of-the-art methods on VIPeR and CUHK01 datasets.

Effectively handles viewpoint and pose variations.

Uses unsupervised saliency estimation methods.

Abstract

Human eyes can recognize person identities based on small salient regions, i.e. human saliency is distinctive and reliable in pedestrian matching across disjoint camera views. However, such valuable information is often hidden when computing similarities of pedestrian images with existing approaches. Inspired by our user study result of human perception on human saliency, we propose a novel perspective for person re-identification based on learning human saliency and matching saliency distribution. The proposed saliency learning and matching framework consists of four steps: (1) To handle misalignment caused by drastic viewpoint change and pose variations, we apply adjacency constrained patch matching to build dense correspondence between image pairs. (2) We propose two alternative methods, i.e. K-Nearest Neighbors and One-class SVM, to estimate a saliency score for each image patch, through which distinctive features stand out without using identity labels in the training procedure. (3) saliency matching is proposed based on patch matching. Matching patches with inconsistent saliency brings penalty, and images of the same identity are recognized by minimizing the saliency matching cost. (4) Furthermore, saliency matching is tightly integrated with patch matching in a unified structural RankSVM learning framework. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. Our approach outperforms the state-of-the-art person re-identification methods on both datasets.