Work

PaMM: Pose-Aware Multi-Shot Matching for Improving Person Re-Identification

Year: 2018

Type: article

Abstract: Person re-identification is the problem of recognizing people across different images or videos with non-overlapping views. Although a significant progress has been made in person re-identification over the last decade, it remains a challenging task because the appearances of people can seem extremely different across diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person re-identification by analyzing camera viewpoints and person poses called pose-aware multi-shot matching. It robustly estimates individual poses and efficiently performs multi-shot matching based on the pose information. The experimental results obtained by using public person re-identification data sets show that the proposed methods outperform the current state-of-the-art methods, and are promising for accomplishing person re-identification under diverse viewpoints and pose variances.

Source: IEEE Transactions on Image Processing

DOI: 10.1109/tip.2018.2815840

Authors: Yeong-Jun Cho, Kuk-Jin Yoon

Institutions: Gwangju Institute of Science and Technology, Korea Advanced Institute of Science and Technology

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Research Foundation of Korea

Award: G207779895