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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.
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: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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