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Indoor future person localization from an egocentric wearable camera

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Title: Indoor future person localization from an egocentric wearable camera
Authors: Qiu, J
Lo, FP-W
Gu, X
Sun, Y
Jiang, S
Lo, B
Item Type: Conference Paper
Abstract: Accurate prediction of future person location and movement trajectory from an egocentric wearable camera can benefit a wide range of applications, such as assisting visually impaired people in navigation, and the development of mobility assistance for people with disability. In this work, a new egocentric dataset was constructed using a wearable camera, with 8,250 short clips of a targeted person either walking 1) toward, 2) away, or 3) across the camera wearer in indoor environments, or 4) staying still in the scene, and 13,817 person bounding boxes were manually labelled. Apart from the bounding boxes, the dataset also contains the estimated pose of the targeted person as well as the IMU signal of the wearable camera at each time point. An LSTM-based encoder-decoder framework was designed to predict the future location and movement trajectory of the targeted person in this egocentric setting. Extensive experiments have been conducted on the new dataset, and have shown that the proposed method is able to reliably and better predict future person location and trajectory in egocentric videos captured by the wearable camera compared to three baselines.
Issue Date: 16-Dec-2021
Date of Acceptance: 1-Dec-2021
URI: http://hdl.handle.net/10044/1/94894
DOI: 10.1109/iros51168.2021.9635868
Publisher: IEEE
Start Page: 8586
End Page: 8592
Journal / Book Title: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Copyright Statement: © 2021 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.
Sponsor/Funder: Bill and Melinda Gates Foundation
Bill & Melinda Gates Foundation
Funder's Grant Number: OPP1171395
Conference Name: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Keywords: cs.CV
Publication Status: Published
Start Date: 2021-09-27
Finish Date: 2021-10-01
Conference Place: Prague, Czech Republic
Online Publication Date: 2021-12-16
Appears in Collections:Department of Surgery and Cancer
Faculty of Medicine
Institute of Global Health Innovation