A framework with updateable joint images re-ranking for Person Re-identification

TL;DR

This paper introduces a dynamic re-ranking framework for person re-identification in surveillance, utilizing an updateable image pool and convolutional features to improve accuracy in real-world scenarios.

Contribution

The proposed framework uniquely integrates image updating rules and convolutional features for improved re-identification performance in surveillance systems.

Findings

Outperforms existing methods on Market-1501, iLIDS-VID, PRID-2011, and ITSD datasets.

Achieves higher rank-1 accuracy and mAP in experiments.

Maintains consistent accuracy in real-world surveillance conditions.

Abstract

Person re-identification plays an important role in realistic video surveillance with increasing demand for public safety. In this paper, we propose a novel framework with rules of updating images for person re-identification in real-world surveillance system. First, Image Pool is generated by using mean-shift tracking method to automatically select video frame fragments of the target person. Second, features extracted from Image Pool by convolutional network work together to re-rank original ranking list of the main image and matching results will be generated. In addition, updating rules are designed for replacing images in Image Pool when a new image satiating with our updating critical formula in video system. These rules fall into two categories: if the new image is from the same camera as the previous updated image, it will replace one of assist images; otherwise, it will replace the main image directly. Experiments are conduced on Market-1501, iLIDS-VID and PRID-2011 and our ITSD datasets to validate that our framework outperforms on rank-1 accuracy and mAP for person re-identification. Furthermore, the update ability of our framework provides consistently remarkable accuracy rate in real-world surveillance system.