short-paper

Recognizing Human Activity in Still Images by Integrating Group-Based Contextual Cues

Authors:

Zheng Zhou,

Kan Li,

Xiangjian HeAuthors Info & Claims

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

Pages 1135 - 1138

https://doi.org/10.1145/2733373.2806300

Published: 13 October 2015 Publication History

Get Access

Abstract

Images with wider angles usually capture more persons in wider scenes, and recognizing individuals' activities in these images based on existing contextual cues usually meet difficulties. We instead construct a novel group-based cue to utilize the context carried by suitable surrounding persons. We propose a global-local cue integration model (GLCIM) to find a suitable group of local cues extracted from individuals and form a corresponding global cue. A fusion restricted Boltzmann machine, a focal subspace measurement and a cue integration algorithm based on entropy are proposed to enable the GLCIM to integrate most of the relevant local cues and least of the irrelevant ones into the group. Our experiments demonstrate how integrating group-based cues improves the activity recognition accuracies in detail and show that all of the key parts of GLCIM make positive contributions to the increases of the accuracies.

References

[1]

M. R. Amer, D. Xie, M. Zhao, S. Todorovic, and S.-C. Zhu. Cost-sensitive top-down/bottom-up inference for multiscale activity recognition. In ECCV, 187--200, 2012.

Digital Library

Google Scholar

[2]

Z. Cheng, L. Qin, Q. Huang, S. Yan, and Q. Tian. Recognizing human group action by layered model with multiple cues. Neurocomputing, 136:124--135, 2014.

Crossref

Google Scholar

[3]

W. Choi, Y.-W. Chao, C. Pantofaru, and S. Savarese. Discovering groups of people in images. In ECCV, 417--433, 2014.

Crossref

Google Scholar

[4]

P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. T-PAMI, 32(9):1627--1645, 2010.

Digital Library

Google Scholar

[5]

G. Guo and A. Lai. A survey on still image based human action recognition. Pattern Recognition, 2014.

Google Scholar

[6]

T. Lan, Y. Wang, W. Yang, S. N. Robinovitch, and G. Mori. Discriminative latent models for recognizing contextual group activities. T-PAMI, 34(8):1549--1562, 2012.

Digital Library

Google Scholar

[7]

L.-J. Li and L. Fei-Fei. What, where and who? classifying events by scene and object recognition. In ICCV, 1--8, 2007.

Crossref

Google Scholar

[8]

T. B. Moeslund, A. Hilton, and V. Krüger. A survey of advances in vision-based human motion capture and analysis. CVIU, 104(2):90--126, 2006.

Digital Library

Google Scholar

[9]

A. Prest, C. Schmid, and V. Ferrari. Weakly supervised learning of interactions between humans and objects. T-PAMI, 34(3):601--614, 2012.

Digital Library

Google Scholar

[10]

N. Shapovalova, W. Gong, M. Pedersoli, F. X. Roca, and J. Gonzàlez. On importance of interactions and context in human action recognition. In Pattern Recognition and Image Analysis, pages 58--66. 2011.

Digital Library

Google Scholar

[11]

T.-H. Vu, C. Olsson, I. Laptev, A. Oliva, and J. Sivic. Predicting actions from static scenes. In ECCV, 421--436, 2014.

Crossref

Google Scholar

[12]

B. Yao and L. Fei-Fei. Recognizing human-object interactions in still images by modeling the mutual context of objects and human poses. T-PAMI, 34(9):1691--1703, 2012.

Digital Library

Google Scholar

[13]

Z.-J. Zha, H. Zhang, M. Wang, H. Luan, and T.-S. Chua. Detecting group activities with multi-camera context. T-CSVT, 23(5):856--869, 2013.

Digital Library

Google Scholar

Cited By

View all

Hysenllari EOttenbacher JMcLennan D(2022)Validation of human activity recognition using a convolutional neural network on accelerometer and gyroscope dataValidierung der Erkennung menschlicher Aktivitäten mittels eines neuronalen Faltungsnetzes anhand von Beschleunigungs- und GyroskopdatenGerman Journal of Exercise and Sport Research10.1007/s12662-022-00817-y52:2(248-252)Online publication date: 6-May-2022
https://doi.org/10.1007/s12662-022-00817-y
Avilés-Cruz CFerreyra-Ramírez AZúñiga-López AVillegas-Cortéz J(2019)Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity RecognitionSensors10.3390/s1907155619:7(1556)Online publication date: 31-Mar-2019
https://doi.org/10.3390/s19071556
Tang YWang ZLi PLu JYang MZhou JBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)Mining Semantics-Preserving Attention for Group Activity RecognitionProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240576(1283-1291)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240576
Show More Cited By

Index Terms

Recognizing Human Activity in Still Images by Integrating Group-Based Contextual Cues
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics
    1. Image manipulation

Recommendations

Quantifying the Contextual Separability of Visual Confusion
AIMS '13: Proceedings of the 2013 1st International Conference on Artificial Intelligence, Modelling and Simulation

Visual information and contextual knowledge can both contribute to the overall performance of an object recognition system. However, little work has so far been done to quantify the extent to which context can compensate for visual error and confusion. ...
Batch-based activity recognition from egocentric photo-streams revisited

Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily ...
Knowledge-Driven Activity Recognition and Segmentation Using Context Connections
The Semantic Web – ISWC 2014
Abstract
We propose a knowledge-driven activity recognition and segmentation framework introducing the notion of context connections. Given an RDF dataset of primitive observations, our aim is to identify, link and classify meaningful contexts that signify ...

Comments

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

National High Technology Research and Development Program of China (973)
Training Program of the Major Project of BIT

Conference

MM '15

Sponsor:

SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

Acceptance Rates

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

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
204
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Hysenllari EOttenbacher JMcLennan D(2022)Validation of human activity recognition using a convolutional neural network on accelerometer and gyroscope dataValidierung der Erkennung menschlicher Aktivitäten mittels eines neuronalen Faltungsnetzes anhand von Beschleunigungs- und GyroskopdatenGerman Journal of Exercise and Sport Research10.1007/s12662-022-00817-y52:2(248-252)Online publication date: 6-May-2022
https://doi.org/10.1007/s12662-022-00817-y
Avilés-Cruz CFerreyra-Ramírez AZúñiga-López AVillegas-Cortéz J(2019)Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity RecognitionSensors10.3390/s1907155619:7(1556)Online publication date: 31-Mar-2019
https://doi.org/10.3390/s19071556
Tang YWang ZLi PLu JYang MZhou JBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)Mining Semantics-Preserving Attention for Group Activity RecognitionProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240576(1283-1291)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240576
Zhou ZLi KHe XLi M(2016)A generative model for recognizing mixed group activities in still imagesProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3061053.3061130(3654-3660)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3061053.3061130

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Quantifying the Contextual Separability of Visual Confusion

Batch-based activity recognition from egocentric photo-streams revisited

Knowledge-Driven Activity Recognition and Segmentation Using Context Connections

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations