research-article

PACE: Prediction-based Annotation for Crowded Environments

Authors:

Federico Bartoli,

Giuseppe Lisanti,

Lorenzo Seidenari,

Alberto Del BimboAuthors Info & Claims

ICMR '17: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval

Pages 121 - 124

https://doi.org/10.1145/3078971.3079020

Published: 06 June 2017 Publication History

Abstract

We present a new tool we have developed to ease the annotation of crowded environments, typical of visual surveillance datasets. Our tool is developed using HTML5 and Javascript and has two back-ends. A PHP based back-end implement the persistence using a relational database and manage the dynamic creation of pages and the authentication procedure. A python based REST server implement all the computer vision facilities to assist annotators. Our tool allows collaborative annotation of person identity, group membership, location, gaze and occluded parts. PACE supports multiple cameras and if calibration is provided the geometry is used to improve computer vision based assistance. We detail the whole interface comprising an administrative view that ease the setup of the system.

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Cited By

Souza JFerreira JSegura V(2023)A Taxonomy of Methods, Tools, and Approaches for Enabling Collaborative AnnotationProceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems10.1145/3638067.3638074(1-12)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3638067.3638074
Tyagi BNigam SSingh R(2022)A Review of Deep Learning Techniques for Crowd Behavior AnalysisArchives of Computational Methods in Engineering10.1007/s11831-022-09772-129:7(5427-5455)Online publication date: 23-Jun-2022
https://doi.org/10.1007/s11831-022-09772-1
Bendali-Braham MWeber JForestier GIdoumghar LMuller P(2021)Recent trends in crowd analysis: A reviewMachine Learning with Applications10.1016/j.mlwa.2021.1000234(100023)Online publication date: Jun-2021
https://doi.org/10.1016/j.mlwa.2021.100023
Show More Cited By

Index Terms

PACE: Prediction-based Annotation for Crowded Environments
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
2. Information systems
  1. World Wide Web
    1. Web interfaces

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Information & Contributors

Information

Published In

cover image ACM Conferences

ICMR '17: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval

June 2017

524 pages

ISBN:9781450347013

DOI:10.1145/3078971

General Chairs:
Bogdan Ionescu
University Politehnica of Bucharest, Romania
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Jiashi Feng
National University of Singapore, Singapore
,
Martha Larson
Radboud University & Delft University of Technology, The Netherlands
,
Rainer Lienhart
University of Augsburg, Germany
,
Cees Snoek
University of Amsterdam & Qualcomm Research Netherlands, The Netherlands

Copyright © 2017 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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2017

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MIUR
Regione Toscana

Conference

ICMR '17

Sponsor:

SIGMM

ICMR '17: International Conference on Multimedia Retrieval

June 6 - 9, 2017

Bucharest, Romania

Acceptance Rates

ICMR '17 Paper Acceptance Rate 33 of 95 submissions, 35%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Souza JFerreira JSegura V(2023)A Taxonomy of Methods, Tools, and Approaches for Enabling Collaborative AnnotationProceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems10.1145/3638067.3638074(1-12)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3638067.3638074
Tyagi BNigam SSingh R(2022)A Review of Deep Learning Techniques for Crowd Behavior AnalysisArchives of Computational Methods in Engineering10.1007/s11831-022-09772-129:7(5427-5455)Online publication date: 23-Jun-2022
https://doi.org/10.1007/s11831-022-09772-1
Bendali-Braham MWeber JForestier GIdoumghar LMuller P(2021)Recent trends in crowd analysis: A reviewMachine Learning with Applications10.1016/j.mlwa.2021.1000234(100023)Online publication date: Jun-2021
https://doi.org/10.1016/j.mlwa.2021.100023
Lee BLee MZhang PTessier AKhan AHarle RFarrahi KLane N(2019)Semantic human activity annotation tool using skeletonized surveillance videosAdjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers10.1145/3341162.3343807(312-315)Online publication date: 9-Sep-2019
https://dl.acm.org/doi/10.1145/3341162.3343807

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