Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/82229
Title: | Person Reidentification Using Multiple Egocentric Views | Authors: | Chakraborty, Anirban Mandal, Bappaditya Yuan, Junsong |
Keywords: | Person reidentification Egocentric videos |
Issue Date: | 2016 | Source: | Chakraborty, A., Mandal, B., & Yuan, J. (2017). Person Reidentification Using Multiple Egocentric Views. IEEE Transactions on Circuits and Systems for Video Technology, 27(3), 484-498. | Series/Report no.: | IEEE Transactions on Circuits and Systems for Video Technology | Abstract: | Development of a robust and scalable multicamera surveillance system is the need of the hour to ensure public safety and security. Being able to reidentify and track one or more targets over multiple nonoverlapping camera field of views in a crowded environment remains an important and challenging problem because of occlusions, large change in the viewpoints, and illumination across cameras. However, the rise of wearable imaging devices has led to new avenues in solving the reidentification (re-id) problem. Unlike static cameras, where the views are often restricted or low resolution and occlusions are common scenarios, egocentric/first person views (FPVs) mostly get zoomed in, unoccluded face images. In this paper, we present a person re-id framework designed for a network of multiple wearable devices. The proposed framework builds on commonly used facial feature extraction and similarity computation methods between camera pairs and utilizes a data association method to yield globally optimal and consistent re-id results with much improved accuracy. Moreover, to ensure its utility in practical applications where a large amount of observations are available every instant, an online scheme is proposed as a direct extension of the batch method. This can dynamically associate new observations to already observed and labeled targets in an iterative fashion. We tested both the offline and online methods on realistic FPV video databases, collected using multiple wearable cameras in a complex office environment and observed large improvements in performance when compared with the state of the arts. | URI: | https://hdl.handle.net/10356/82229 http://hdl.handle.net/10220/43503 |
ISSN: | 1051-8215 | DOI: | 10.1109/TCSVT.2016.2615445 | Schools: | School of Electrical and Electronic Engineering | Research Centres: | Rapid-Rich Object Search Lab | Rights: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TCSVT.2016.2615445]. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Journal Articles |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Person Reidentification Using Multiple Egocentric Views.pdf | 4.43 MB | Adobe PDF | View/Open |
SCOPUSTM
Citations
50
5
Updated on Mar 21, 2024
Web of ScienceTM
Citations
50
4
Updated on Oct 26, 2023
Page view(s) 50
448
Updated on Mar 28, 2024
Download(s) 20
202
Updated on Mar 28, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.