research-article

The MERL motion detector dataset

Authors:

Christopher R. Wren,

Yuri A. Ivanov,

Jonathan WesthuesAuthors Info & Claims

MD '07: Proceedings of the 2007 workshop on Massive datasets

Pages 10 - 14

https://doi.org/10.1145/1352922.1352926

Published: 15 November 2007 Publication History

Abstract

Looking into the future of residential and office building Mitsubishi Electric Research Labs (MERL) has been collecting motion sensor data from a network of over 200 sensors for a year. The data is the residual traces of year in the life of a research laboratory. It contains interesting spatio-temporal structure ranging all the way from the seconds of individuals walking down hallways, the minutes in lobbies chatting with colleagues, the hours of dozens of people attending talks and meetings, the days and weeks that drive the patterns of life, to the months and seasons with their ebb and flow of visiting employees. This document describes that dataset, which contains well over 30 million raw motion records, spanning a calendar year and two floors of our research laboratory, as well as calender, weather, and some intermediate analytic results. The dataset was originally released as part of the 2007 Workshop on Massive Datasets. The dataset can be obtained from http://www.merl.com/wmd.

References

[1]

G. Abowd, A. Bobick, I. Essa, E. Mynatt, and W. Rogers. The aware home: Developing technologies for successful aging. In Proceedings of AAAI Workshop on Automation as a Care Giver, 2002.

[2]

R. Aipperspach, E. Cohen, and J. Canny. Modeling human behavior from simple sensors in the home. In Proceedings Of The IEEE Conference On Pervasive Computing, 2006.

Digital Library

[3]

A. Baumberg and D. Hogg. An efficient method for contour tracking using active shape models. In Proceeding of the Workshop on Motion of Nonrigid and Articulated Objects. IEEE Computer Society, 1994.

[4]

A. F. Bobick. Movement, activity and action: the role of knowledge in the perception of motion. Philosophical Transactions: Biological Sciences, 352(1358):1257--1265, 1997.

[5]

T. Choudhury and A. Pentland. Characterizing social networks using the sociometer. In Proceedings of the North American Association of Computational Social and Organizational Science (NAACSOS), 2004.

[6]

G. C. de Silva, T. Yamasaki, and K. Aizawa. An interactive multimedia diary for the home. Computer, 40(5):52--59, 2007.

Digital Library

[7]

N. Eagle and A. Pentland. Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing, 10(4):255--268, 2006.

Digital Library

[8]

I. Essa. Ubiquitous sensing for smart and aware environments. IEEE Personal Communications, October 2000. Special Issue on Networking the Physical World.

[9]

D. Estrin, R. Govindan, J. Heidemann, and S. Kumar. Next century challenges: scalable coordination in sensor networks. In MobiCom '99: Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking, pages 263--270, New York, NY, USA, 1999. ACM, ACM Press.

Digital Library

[10]

J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. E. Culler, and K. S. J. Pister. System architecture directions for networked sensors. In Architectural Support for Programming Languages and Operating Systems, pages 93--104, 2000.

Digital Library

[11]

Y. A. Ivanov, C. R. Wren, A. Sorokin, and I. Kaur. Visualizing the history of living spaces. Transactions on Visualization and Computer Graphics, 13(6):1153--1160, 2007.

Digital Library

[12]

I. Kaur. Openspace: Enhancing social awareness at the workplace. Master's thesis, Massachusetts Institute of Technology, 2007.

[13]

S.-N. Lim, L. S. Davis, and A. Elgammal. A scalable image-based multi-camera visual surveillance system. In IEEE AVSS, Miami, Florida, July 2003.

Digital Library

[14]

T. Mori, K. Asaki, H. Noguchi, and T. Sato. Accumulation and summarization of human daily action data in one-room-type sensing system. Intelligent Robots and Systems, 2001. Proceedings. 2001 IEEE/RSJ International Conference on, 4:2349--2354, 2001.

[15]

E. Munguia Tapia, S. S. Intille, L. Lopez, and K. Larson. The design of a portable kit of wireless sensors for naturalistic data collection. In Proceedings of PERVASIVE 2006, Dublin, Ireland, 2006. Springer-Verlag.

Digital Library

[16]

A. Rahimi, B. Dunagan, and T. Darrell. Simultaneous calibration and tracking with a network of non-overlapping sensors. In Computer Vision and Pattern Recognition, pages 187--194. IEEE Computer Society, June 2004.

[17]

C. J. Reynolds and C. R. Wren. Worse is better for ambient sensing. In Pervasive: Workshop on Privacy, Trust and Identity Issues for Ambient Intelligence, May 2006.

[18]

H. S. Sawhney, A. Arpa, R. Kumar, S. Samarasekera, M. Aggarwal, S. Hsu, D. Nister, and K. Hanna. Video flashlights: real time rendering of multiple videos for immersive model visualization. In Proceedings of the 13th Eurographics workshop on Rendering, pages 157--168, 2002.

Digital Library

[19]

C. Stauffer and K. Tieu. Automated multi-camera planar tracking correspondence modeling. In Computer Vision and Pattern Recognition, pages 259--266. IEEE, July 2003.

Digital Library

[20]

D. H. Wilson and C. Atkeson. Simultaneous tracking & activity recognition (star) using many anonymous, binary sensors. In The Third International Conference on Pervasive Computing, pages 62--79, 2005.

Digital Library

[21]

C. R. Wren, U. M. Erdem, and A. J. Azarbayejani. Functional calibration for pan-tilt-zoom cameras in hybrid sensor networks. ACM Multimedia Systems Journal, 12(3):255--268, December 2006.

Digital Library

[22]

C. R. Wren, Y. A. Ivanov, D. Leigh, and J. Westhues. Buzz: measuring and visualizing conference crowds. In SIGGRAPH '07: ACM SIGGRAPH 2007 emerging technologies, page 25, New York, NY, USA, 2007. ACM.

Digital Library

[23]

C. R. Wren, Y. A. Ivanov, D. Leigh, and J. Westhues. The merl motion detector dataset: 2007 workshop on massive datasets. Technical Report TR2007-069, Mitsubishi Electric Research Laboratories, Cambridge, MA, USA, August 2007.

Digital Library

[24]

C. R. Wren, Y. A. Ivanov, D. Leigh, and J. Westhues. The merl motion detector dataset: 2007 workshop on massive datasets. Technical Report 069, Mitsubishi Electric Research Laboratories, 2007.

Digital Library

[25]

C. R. Wren, D. Minnen, and S. G. Rao. Similarity-based analysis for large networks of ultra-low resolution sensors. Pattern Recognition, 39(10):1918--1931, October 2006. Special Issue on Similarity-Based Pattern Recognition.

Digital Library

[26]

C. R. Wren and E. Munguia Tapia. Toward scalable activity recognition for sensor networks. In Lecture Notes in Computer Science, Volume 3987, pages 168--185, Dublin, Ireland, 2006. 2nd International Workshop on Location- and Context-Awareness.

Digital Library

Cited By

Laidi RDjenouri DDjenouri YLin J(2024)TG-SPRED: Temporal Graph for Sensorial Data PREDictionACM Transactions on Sensor Networks10.1145/364989220:3(1-20)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3649892
Qi SMonis LZeng ZWang IRamakrishnan K(2024)SPRIGHT: High-Performance eBPF-Based Event-Driven, Shared-Memory Processing for Serverless ComputingIEEE/ACM Transactions on Networking10.1109/TNET.2024.336656132:3(2539-2554)Online publication date: Jun-2024
https://doi.org/10.1109/TNET.2024.3366561
Qi SMonis LZeng ZWang IRamakrishnan KKuipers FOrda A(2022)SPRIGHTProceedings of the ACM SIGCOMM 2022 Conference10.1145/3544216.3544259(780-794)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1145/3544216.3544259
Show More Cited By

Index Terms

The MERL motion detector dataset

Recommendations

A collection of challenging motion segmentation benchmark datasets

An in-depth analysis of computer vision methodologies is greatly dependent on the benchmarks they are tested upon. Any dataset is as good as the diversity of the true nature of the problem enclosed in it. Motion segmentation is a preprocessing step in ...
A Factorization-Based Approach for Articulated Nonrigid Shape, Motion and Kinematic Chain Recovery From Video

Recovering articulated shape and motion, especially human body motion, from video is a challenging problem with a wide range of applications in medical study, sport analysis and animation, etc. Previous work on articulated motion recovery generally ...
Workshop Summary of AISec'14: 2014 Workshop on Artificial Intelligent and Security
CCS '14: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security

It is our great pleasure to welcome you to the 2014 ACM Workshop Artificial Intelligence and Security (AISec 2014) -- the seventh annual workshop addressing technologies that fuse intelligent systems into computer security applications and the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MD '07: Proceedings of the 2007 workshop on Massive datasets

November 2007

15 pages

ISBN:9781595938718

DOI:10.1145/1352922

Copyright © 2007 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

Mitsubishi Electric Research Laboratories
ICMI Organizers
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICMI07

Sponsor:

SIGCHI

ICMI07: International Conference on Multimodal Interface

November 15, 2007

Nagoya, Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

42
Total Citations
View Citations
371
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 22 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Laidi RDjenouri DDjenouri YLin J(2024)TG-SPRED: Temporal Graph for Sensorial Data PREDictionACM Transactions on Sensor Networks10.1145/364989220:3(1-20)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3649892
Qi SMonis LZeng ZWang IRamakrishnan K(2024)SPRIGHT: High-Performance eBPF-Based Event-Driven, Shared-Memory Processing for Serverless ComputingIEEE/ACM Transactions on Networking10.1109/TNET.2024.336656132:3(2539-2554)Online publication date: Jun-2024
https://doi.org/10.1109/TNET.2024.3366561
Qi SMonis LZeng ZWang IRamakrishnan KKuipers FOrda A(2022)SPRIGHTProceedings of the ACM SIGCOMM 2022 Conference10.1145/3544216.3544259(780-794)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1145/3544216.3544259
Laidi RDjenouri DBalasingham I(2022)On Predicting Sensor Readings With Sequence Modeling and Reinforcement Learning for Energy-Efficient IoT ApplicationsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2021.311614152:8(5140-5151)Online publication date: Aug-2022
https://doi.org/10.1109/TSMC.2021.3116141
Djenouri DLaidi RDjenouri Y(2022)Deep Learning for Estimating Sleeping Sensor’s Values in Sustainable IoT Applications2022 International Balkan Conference on Communications and Networking (BalkanCom)10.1109/BalkanCom55633.2022.9900817(147-151)Online publication date: 22-Aug-2022
https://doi.org/10.1109/BalkanCom55633.2022.9900817
Hong RChandra ALaure EMarkidis SVerbanescu ALofstead G(2021)DLionProceedings of the 30th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3431379.3460643(227-238)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3431379.3460643
Marks JMontano BChong JRaavi MIslam RCerny TShin DHung CHong JBechini ASong E(2021)Differential privacy applied to smart metersProceedings of the 36th Annual ACM Symposium on Applied Computing10.1145/3412841.3442360(761-770)Online publication date: 22-Mar-2021
https://dl.acm.org/doi/10.1145/3412841.3442360
Flamino JSzymanski BBahulkar AChan KLizardo O(2021)Creation, evolution, and dissolution of social groupsScientific Reports10.1038/s41598-021-96805-711:1Online publication date: 1-Sep-2021
https://doi.org/10.1038/s41598-021-96805-7
Teh HKempa-Liehr AWang K(2020)Sensor data quality: a systematic reviewJournal of Big Data10.1186/s40537-020-0285-17:1Online publication date: 11-Feb-2020
https://doi.org/10.1186/s40537-020-0285-1
Mohammady MXie SHong YZhang MWang LPourzandi MDebbabi MLigatti JOu XKatz JVigna G(2020)R2DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal DistributionsProceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security10.1145/3372297.3417259(677-696)Online publication date: 30-Oct-2020
https://dl.acm.org/doi/10.1145/3372297.3417259
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.

Figures

Tables

Media

View Table of Conten