Person Re-identification Using Robust Brightness Transfer Functions Based on Multiple Detections

Autor:	Alessandro Perina, Vittorio Murino, Amran Bhuiyan, Behzad Mirmahboub
Rok vydání:	2015
Předmět:	Brightness business.industry Computer science Video surveillance ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Multiple frame Brightness transfer function Re-identification Transfer function Re identification Transfer (computing) Computer vision Artificial intelligence business
Zdroj:	Image Analysis and Processing — ICIAP 2015 ISBN: 9783319232331 ICIAP (2)
DOI:	10.1007/978-3-319-23234-8_42
Popis:	Re-identification systems aim at recognizing the same individuals in multiple cameras and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of Minimum Multiple Cumulative Brightness Transfer Functions to model this appearance variations. It is multiple frame-based learning approach which leverages consecutive detections of each individual to transfer the appearance, rather than learning brightness transfer function from pairs of images. We tested our approach on standard multi-camera surveillance datasets showing consistent and significant improvements over existing methods on two different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8eda5ff26bd2b9169f4175d975d7d5d9 https://doi.org/10.1007/978-3-319-23234-8_42 Zobrazit plný text záznamu