research-article

Event geo-localization and tracking from crowd-sourced video metadata

Authors:

Subhasis ChaudhuriAuthors Info & Claims

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 24, Pages 1 - 8

https://doi.org/10.1145/3009977.3009993

Published: 18 December 2016 Publication History

Abstract

We propose a novel technique for event geo-localization (i.e. 2-D location of the event on the surface of the earth) from the sensor metadata of crowd-sourced videos collected from smartphone devices. With the help of sensors available in the smartphone devices, such as digital compass and GPS receiver, we collect metadata information such as camera viewing direction and location along with the video. The event localization is then posed as a constrained optimization problem using available sensor metadata. Our results on the collected experimental data shows correct localization of events, which is particularly challenging for classical vision based methods because of the nature of the visual data. Since we only use sensor metadata in our approach, computational overhead is much less compared to what would be if video information is used. At the end, we illustrate the benefits of our work in analyzing the video data from multiple sources through geo-localization.

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Cited By

More AChaudhuri S(2019)A Pseudo-likelihood Approach for Geo-localization of Events from Crowd-sourced Sensor-MetadataACM Transactions on Multimedia Computing, Communications, and Applications10.1145/332170115:3(1-26)Online publication date: 20-Aug-2019
https://dl.acm.org/doi/10.1145/3321701
More AChaudhuri S(2018)A Neural Network Based Approach for Geo-Localizing Events in Crowd Sourced VideosProceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing10.1145/3293353.3293365(1-9)Online publication date: 18-Dec-2018
https://dl.acm.org/doi/10.1145/3293353.3293365

Index Terms

Event geo-localization and tracking from crowd-sourced video metadata

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cover image ACM Other conferences

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

December 2016

743 pages

ISBN:9781450347532

DOI:10.1145/3009977

Program Chairs:
Dhruv Batra
Georgia Tech
,
Michael Brown
York University, Canada
,
Vijay Natarajan
Indian Institute of Science, Bangalore

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Google Inc.
QI: Qualcomm Inc.
Tata Consultancy Services
NVIDIA
MathWorks: The MathWorks, Inc.
Microsoft Research: Microsoft Research

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2016

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ICVGIP '16

Sponsor:

QI
MathWorks
Microsoft Research

ICVGIP '16: Indian Conference on Computer Vision, Graphics and Image Processing

December 18 - 22, 2016

Assam, Guwahati, India

Acceptance Rates

ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;

Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

More AChaudhuri S(2019)A Pseudo-likelihood Approach for Geo-localization of Events from Crowd-sourced Sensor-MetadataACM Transactions on Multimedia Computing, Communications, and Applications10.1145/332170115:3(1-26)Online publication date: 20-Aug-2019
https://dl.acm.org/doi/10.1145/3321701
More AChaudhuri S(2018)A Neural Network Based Approach for Geo-Localizing Events in Crowd Sourced VideosProceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing10.1145/3293353.3293365(1-9)Online publication date: 18-Dec-2018
https://dl.acm.org/doi/10.1145/3293353.3293365

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