Person detection and re-identification in open-world settings of retail stores and public spaces

TL;DR

This paper discusses the challenges and solutions for person detection and re-identification in open-world environments like retail and public spaces, emphasizing system complexity, real-time performance, and practical applications.

Contribution

It analyzes existing challenges and proposes solutions for person re-identification in open-world settings, including real-time system performance and applications in retail and public spaces.

Findings

Demonstrated near real-time re-identification performance across multiple video feeds.

Identified key challenges in system design for open-world person re-identification.

Suggested future research directions for system improvements.

Abstract

Practical applications of computer vision in smart cities usually assume system integration and operation in challenging open-world environments. In the case of person re-identification task the main goal is to retrieve information whether the specific person has appeared in another place at a different time instance of the same video, or over multiple camera feeds. This typically assumes collecting raw data from video surveillance cameras in different places and under varying illumination conditions. In the considered open-world setting it also requires detection and localization of the person inside the analyzed video frame before the main re-identification step. With multi-person and multi-camera setups the system complexity becomes higher, requiring sophisticated tracking solutions and re-identification models. In this work we will discuss existing challenges in system design architectures, consider possible solutions based on different computer vision techniques, and describe applications of such systems in retail stores and public spaces for improved marketing analytics. In order to analyse sensitivity of person re-identification task under different open-world environments, a performance of one close to real-time solution will be demonstrated over several video captures and live camera feeds. Finally, based on conducted experiments we will indicate further research directions and possible system improvements.