Article

Unsupervised analysis of activity sequences using event-motifs

Authors:

Siddhartha Maddi,

Irfan EssaAuthors Info & Claims

VSSN '06: Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks

Pages 71 - 78

https://doi.org/10.1145/1178782.1178794

Published: 27 October 2006 Publication History

Abstract

We present an unsupervised framework to discover characterizations of everyday human activities, and demonstrate how such representations can be used to extract points of interest in event-streams. We begin with the usage of Suffix Trees as an efficient activity-representation to analyze the global structural information of activities, using their local event statistics over the entire continuum of their temporal resolution. Exploiting this representation, we discover characterizing event-subsequences and present their usage in an ensemble-based framework for activity classification. Finally, we propose a method to automatically detect subsequences of events that are locally atypical in a structural sense. Results over extensive data-sets, collected from multiple sensor-rich environments are presented, to show the competence and scalability of the proposed framework.

References

[1]

A. Apostolico, M. Bock, S. Lonardi, and X. Xu. Efficient detection of unusual words. J. Computational Biology,7,1/2: 155--212, January.

[2]

J. Auguston and J. Miker. An analysis of some graph theoretical clustering techniques. J. ACM, 17(4):571--588, 1970.

Digital Library

[3]

D. Gusfield. Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge University Press; 1st edition, 1997.

Digital Library

[4]

R. Hamid, A. Johnson, S. Batta, A. Bobick, C. Isbell, and G. Coleman. Detection and explanation of anomalous activities: Representing activities as bags of event n-grams. In IEEE CVPR, 2005.

Digital Library

[5]

R. Hamid, S. Maddi, A. Johnson, A. Bobick, I. Essa, and C. Isbell. Discovery and characterization of activities from event-streams. In Conference on Uncertainty in AI (UAI), 2005.

[6]

M. Isard and J. MacCormick. Bramble: A bayesian multiple-blob tracker. In ICCV, 2001

[7]

D. Moore and I. Essa. Recognizing multitasked activities using stochastic context-free grammar,using video. In AAAI, 2002.

Digital Library

[8]

M. Pavan and M. Pelillo. A new graph-theoretic approach to clustering and segmentation. In CVPR 2003.

Digital Library

[9]

E. Rosch, C. Mervis, W. Gray, D. Johnson, and P. Boyes-Braem. Basic objects in natural categories. Cognitive Psychology, 8, 1976.

[10]

G. Salton. The SMART Retrieval System - Experiment in Automatic Document Processing. Prentice-Hall, Englewood Cliffs, New Jersey, 1971.

Digital Library

[11]

R. E. Schapire and Y. Singer. BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2/3):135--168, 2000.

Digital Library

[12]

Y. Shi, Y. Huang, D. Minen, A. Bobick, and I. Essa. Propogation networks for recognizing partially ordered sequential action. In In Proceedings of IEEE Conference on CVPR,2004.

Digital Library

[13]

T. Starner, J. Weaver, and A. Pentland. Real-time american sign language recognition using desk and wearable computer based video. PAMI, 20: 1371--1375, 1998.

Digital Library

[14]

C. Stauffer and W. Grimson. Learning patterns of activity using real-time tracking. PAMI, 22(8):747--757, 2000.

Digital Library

[15]

E. Ukkonen. Constructing suffix trees on-line in linear time. In Proc.Information Processing 92, Vol.1, IFIP Transactions A-12 484--492, 1994.

Digital Library

[16]

S. Watanabe. Pattern recognition: Human and mechanical. Wiley & Sons, New York, 1985.

Digital Library

[17]

Y. Weiss. Segmentation using eigen vectors: a unifying view. In ICCV, 1999.

Digital Library

[18]

H. Zhong, J. Shi, and M. Visontai. Detecting unusual activity in video. In Proc.of IEEE CVPR, 2004.

Digital Library

Cited By

Gu TChen STao XLu J(2019)An unsupervised approach to activity recognition and segmentation based on object-use fingerprintsData & Knowledge Engineering10.1016/j.datak.2010.01.00469:6(533-544)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1016/j.datak.2010.01.004
Domingo CSee SLegaspi R(2018)Unsupervised Habitual Activity Detection in Accelerometer DataMechatronics and Machine Vision in Practice 310.1007/978-3-319-76947-9_19(253-272)Online publication date: 5-Apr-2018
https://doi.org/10.1007/978-3-319-76947-9_19
Dimaggio MLeotta FMecella MSora D(2016)Process-Based Habit Mining: Experiments and Techniques2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0043(145-152)Online publication date: Jul-2016
https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0043
Show More Cited By

Index Terms

Unsupervised analysis of activity sequences using event-motifs
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

Recommendations

Towards unobtrusive detection and realistic attribute analysis of daily activity sequences using a finger-worn device

Detection and analysis of activities of daily living (ADLs) are important in activity tracking, security monitoring, and life support in elderly healthcare. Recently, many research projects have employed wearable devices to detect and analyze ADLs. ...
Unsupervised activity discovery and characterization from event-streams
UAI'05: Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence

We present a framework to discover and characterize different classes of everyday activities from event-streams. We begin by representing activities as bags of event n-grams. This allows us to analyze the global structural information of activities, ...
Towards unsupervised physical activity recognition using smartphone accelerometers

The development of smartphones equipped with accelerometers gives a promising way for researchers to accurately recognize an individual's physical activity in order to better understand the relationship between physical activity and health. However, a ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

VSSN '06: Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks

October 2006

230 pages

ISBN:1595934960

DOI:10.1145/1178782

General Chairs:
Jake K. Aggarwal
University of Texas
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chair:
Andrea Prati
University of Modena and Reggio Emilia, Italy

Copyright © 2006 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

MM06

Sponsor:

MM06: The 14th ACM International Conference on Multimedia 2006

October 27, 2006

California, Santa Barbara, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
355
Total Downloads

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

Reflects downloads up to 30 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gu TChen STao XLu J(2019)An unsupervised approach to activity recognition and segmentation based on object-use fingerprintsData & Knowledge Engineering10.1016/j.datak.2010.01.00469:6(533-544)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1016/j.datak.2010.01.004
Domingo CSee SLegaspi R(2018)Unsupervised Habitual Activity Detection in Accelerometer DataMechatronics and Machine Vision in Practice 310.1007/978-3-319-76947-9_19(253-272)Online publication date: 5-Apr-2018
https://doi.org/10.1007/978-3-319-76947-9_19
Dimaggio MLeotta FMecella MSora D(2016)Process-Based Habit Mining: Experiments and Techniques2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0043(145-152)Online publication date: Jul-2016
https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0043
Zhu YNayak NRoy-Chowdhury A(2013)Context-Aware Activity Recognition and Anomaly Detection in VideoIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2012.22347227:1(91-101)Online publication date: Feb-2013
https://doi.org/10.1109/JSTSP.2012.2234722
Gu TWang LChen HTao XLu J(2011)Recognizing Multiuser Activities Using Wireless Body Sensor NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2011.4310:11(1618-1631)Online publication date: 1-Nov-2011
https://dl.acm.org/doi/10.1109/TMC.2011.43
Gu TWang LWu ZTao XLu J(2011)A Pattern Mining Approach to Sensor-Based Human Activity RecognitionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2010.18423:9(1359-1372)Online publication date: Sep-2011
https://doi.org/10.1109/TKDE.2010.184
Zoumboulakis MRoussos G(2011)Pattern Detection in Extremely Resource-Constrained DevicesReasoning in Event-Based Distributed Systems10.1007/978-3-642-19724-6_9(195-216)Online publication date: 2011
https://doi.org/10.1007/978-3-642-19724-6_9
Almasalha FBashir FKhokhar AKhan FHaseeb HGhafoor A(2010)Semantic Analysis for Multimedia Security ApplicationSemantic Computing10.1002/9780470588222.ch21(439-463)Online publication date: 20-Jul-2010
https://doi.org/10.1002/9780470588222.ch21
Tziakos ICavallaro AXu L(2009)Video event segmentation and visualisation in non-linear subspacePattern Recognition Letters10.1016/j.patrec.2008.02.02830:2(123-131)Online publication date: Jan-2009
https://doi.org/10.1016/j.patrec.2008.02.028
Kamei KYanagisawa YMaekawa TKishino YSakurai YOkadome TBarthelmess PKaiser E(2007)Tagging strategies for extracting real-world events with networked sensorsProceedings of the 2007 workshop on Tagging, mining and retrieval of human related activity information10.1145/1330588.1330594(35-42)Online publication date: 15-Nov-2007
https://dl.acm.org/doi/10.1145/1330588.1330594
Show More Cited By

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.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents