short-paper

Jointly Estimating Interactions and Head, Body Pose of Interactors from Distant Social Scenes

Authors:

Ramanathan Subramanian,

Jagannadan Varadarajan,

Elisa Ricci,

Oswald Lanz, and

Stefan WinklerAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

Pages 835 - 838

https://doi.org/10.1145/2733373.2806343

Published: 13 October 2015 Publication History

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Abstract

We present joint estimation of F-formations and head, body pose of interactors in a social scene captured by surveillance cameras. Unlike prior works that have focused on (a) discovering F-formations based on head pose and position cues, or (b) jointly learned head and body pose of individuals based on anatomic constraints, we exploit positional and pose cues characterizing interactors and interactions to jointly infer both (a) and (b). We show how the joint inference framework benefits both F-formation and head, body pose estimation accuracy via experiments on two social datasets.

References

[1]

X. Alameda-Pineda, J. Staiano, R. Subramanian, L. M. Batrinca, E. Ricci, B. Lepri, O. Lanz, and N. Sebe. SALSA: A novel dataset for multimodal group behavior analysis. CoRR, abs, 2015.

Google Scholar

[2]

B. Benfold and I. Reid. Unsupervised learning of a scene-specific coarse gaze estimator. In ICCV, 2011.

Digital Library

Google Scholar

[3]

C. Chen and J. M. Odobez. We are not contortionists: coupled adaptive learning for head and body orientation estimation in surveillance video. In CVPR, 2012.

Digital Library

Google Scholar

[4]

M. Cristani, L. Bazzani, G. Paggetti, A. Fossati, D. Tosato, A. Del Bue, G. Menegaz, and V. Murino. Social interaction discovery by statistical analysis of f-formations. In BMVC, 2011.

Crossref

Google Scholar

[5]

A. K. Rajagopal, R. Subramanian, E. Ricci, R. L. Vieriu, O. Lanz, N. Sebe, et al. Exploring transfer learning approaches for head pose classification from multi-view surveillance images. IJCV, 109(1--2):146--167, 2014.

Digital Library

Google Scholar

[6]

F. Setti, H. Hung, and M. Cristani. Group detection in still images by f-formation modeling: A comparative study. In WIAMIS, 2013.

Crossref

Google Scholar

[7]

F. Setti, O. Lanz, R. Ferrario, V. Murino, and M. Cristani. Multi-scale f-formation discovery for group detection. In ICIP, 2013.

Crossref

Google Scholar

[8]

J. Staiano, B. Lepri, R. Subramanian, N. Sebe, and F. Pianesi. Automatic modeling of personality states in small group interactions. In ACMMM, 2011.

Digital Library

Google Scholar

[9]

S. Vascon, E. Z. Mequanint, M. Cristani, H. Hung, M. Pelillo, and V. Murino. A game theoretic probabilistic approach for detecting conversational groups. In ACCV, 2014.

Google Scholar

[10]

Y. Yan, E. Ricci, R. Subramanian, O. Lanz, and N. Sebe. No matter where you are: Flexible graph-guided multi-task learning for multi-view head pose classification under target motion. In ICCV, 2013.

Digital Library

Google Scholar

[11]

G. Zen, B. Lepri, E. Ricci, and O. Lanz. Space speaks: towards socially and personality aware visual surveillance. In MPVA. ACM, 2010.

Digital Library

Google Scholar

[12]

X. Zhu and A. B. Goldberg. Introduction to semi-supervised learning. Synthesis lectures on artificial intelligence and machine learning, 3(1):1--130, 2009.

Digital Library

Google Scholar

Cited By

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Beyan CVinciarelli ABue A(2023)Co-Located Human–Human Interaction Analysis Using Nonverbal Cues: A SurveyACM Computing Surveys10.1145/362651656:5(1-41)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3626516
Era TYoshimura HNishiyama MIwai Y(2017)Inferring state transition from bystander to participant in free-style conversational interaction2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS.2017.8078480(1-6)Online publication date: Aug-2017
https://doi.org/10.1109/AVSS.2017.8078480

Index Terms

Jointly Estimating Interactions and Head, Body Pose of Interactors from Distant Social Scenes
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
    2. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

Information

Published In

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

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Agency for Science Technology and Research

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MM '15

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SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

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MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

Overall Acceptance Rate 995 of 4,171 submissions, 24%

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The 32nd ACM International Conference on Multimedia

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Cited By

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Beyan CVinciarelli ABue A(2023)Co-Located Human–Human Interaction Analysis Using Nonverbal Cues: A SurveyACM Computing Surveys10.1145/362651656:5(1-41)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3626516
Era TYoshimura HNishiyama MIwai Y(2017)Inferring state transition from bystander to participant in free-style conversational interaction2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS.2017.8078480(1-6)Online publication date: Aug-2017
https://doi.org/10.1109/AVSS.2017.8078480

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Joint Estimation of Human Pose and Conversational Groups from Social Scenes

Social Grouping for Multi-Target Tracking and Head Pose Estimation in Video