Design and implementation of a sensor-based wireless camera system for continuous monitoring in assistive environments
Camera-based surveillance system is an important tool for assistive environment to monitor those who may have physical or cognitive impairment. It is, however, expensive to deploy a wired surveillance system and difficult to continuously monitor a moving subject in a large facility where many camera...
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Published in | Personal and ubiquitous computing Vol. 14; no. 6; pp. 499 - 510 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
London
Springer-Verlag
01.09.2010
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 1617-4909 1617-4917 |
DOI | 10.1007/s00779-009-0271-2 |
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Abstract | Camera-based surveillance system is an important tool for assistive environment to monitor those who may have physical or cognitive impairment. It is, however, expensive to deploy a wired surveillance system and difficult to continuously monitor a moving subject in a large facility where many cameras are deployed. In this paper, we first evaluate the performance of streaming camera images over wireless networks in both residential and office environments and present the quantitative results to show the feasibility of using wireless backbones for camera surveillance systems. We then propose sensor-integrated camera surveillance (SICS) to address the continuous monitoring problem. SICS uses wearable wireless sensors to locate moving subjects and automatically selects the camera covering the subject, allowing human operators to focus on only one screen to monitor an individual. SICS uses a self-organizing wireless mesh network to allow flexible deployment at reduced cost. An on-board image-processing algorithm is used to reduce the bandwidth consumption. Through empirical evaluation, we found that the automatic camera hand-off enabled by SICS was effective for continuous camera monitoring and a sophisticated wireless network management system is required to deploy the SICS in practice. |
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AbstractList | Camera-based surveillance system is an important tool for assistive environment to monitor those who may have physical or cognitive impairment. It is, however, expensive to deploy a wired surveillance system and difficult to continuously monitor a moving subject in a large facility where many cameras are deployed. In this paper, we first evaluate the performance of streaming camera images over wireless networks in both residential and office environments and present the quantitative results to show the feasibility of using wireless backbones for camera surveillance systems. We then propose sensor-integrated camera surveillance (SICS) to address the continuous monitoring problem. SICS uses wearable wireless sensors to locate moving subjects and automatically selects the camera covering the subject, allowing human operators to focus on only one screen to monitor an individual. SICS uses a self-organizing wireless mesh network to allow flexible deployment at reduced cost. An on-board image-processing algorithm is used to reduce the bandwidth consumption. Through empirical evaluation, we found that the automatic camera hand-off enabled by SICS was effective for continuous camera monitoring and a sophisticated wireless network management system is required to deploy the SICS in practice. Camera-based surveillance system is an important tool for assistive environment to monitor those who may have physical or cognitive impairment. It is, however, expensive to deploy a wired surveillance system and difficult to continuously monitor a moving subject in a large facility where many cameras are deployed. In this paper, we first evaluate the performance of streaming camera images over wireless networks in both residential and office environments and present the quantitative results to show the feasibility of using wireless backbones for camera surveillance systems. We then propose sensor-integrated camera surveillance (SICS) to address the continuous monitoring problem. SICS uses wearable wireless sensors to locate moving subjects and automatically selects the camera covering the subject, allowing human operators to focus on only one screen to monitor an individual. SICS uses a self-organizing wireless mesh network to allow flexible deployment at reduced cost. An on-board image-processing algorithm is used to reduce the bandwidth consumption. Through empirical evaluation, we found that the automatic camera hand-off enabled by SICS was effective for continuous camera monitoring and a sophisticated wireless network management system is required to deploy the SICS in practice. [PUBLICATION ABSTRACT] |
Author | Li, Nan Chen, Guanling Yan, Bo Govindaswamy, Prabhu Wang, Jie |
Author_xml | – sequence: 1 givenname: Nan surname: Li fullname: Li, Nan email: nli@cs.uml.edu organization: Computer Science Department, University of Massachusetts Lowell – sequence: 2 givenname: Bo surname: Yan fullname: Yan, Bo organization: Computer Science Department, University of Massachusetts Lowell – sequence: 3 givenname: Guanling surname: Chen fullname: Chen, Guanling organization: Computer Science Department, University of Massachusetts Lowell – sequence: 4 givenname: Prabhu surname: Govindaswamy fullname: Govindaswamy, Prabhu organization: Qualcomm – sequence: 5 givenname: Jie surname: Wang fullname: Wang, Jie organization: Computer Science Department, University of Massachusetts Lowell |
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Cites_doi | 10.1109/18.825799 10.1016/j.comcom.2008.01.046 10.1145/1101149.1101191 10.1155/2007/31976 10.1109/SAHCN.2004.1381963 10.1145/1080829.1080837 10.1145/1080829.1080835 10.1145/1098918.1098939 10.1145/1080829.1080849 10.1109/ICICS.2003.1292637 10.1145/1023720.1023728 10.1007/s00779-006-0095-2 10.1109/2.940013 10.1109/INFOCOM.2006.293 10.1145/957013.957132 10.1145/1163593.1163597 10.1145/1159913.1159920 10.1109/IPSN.2005.1440970 |
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References_xml | – reference: A profile of older Americans (2007) Administration on aging, US Department of Health and Human Services. http://www.agingcarefl.org/aging/AOA-2007profile.pdf – reference: PollackMEIntelligent technology for an aging population: the use of ai to assist elders with cognitive impairment.AI Magazine2005262924 – reference: Ramachandran K, Belding-Royer E, Almeroth K (2004) DAMON: A distributed architecture for monitoring multi-hop mobile networks. In: Proceedings of the 1st IEEE international conference on sensor and Ad Hoc communications and networks. Santa Clara, CA, pp 601–609. http://ieeexplore.ieee.org/iel5/9491/30129/01381963.pdf – reference: Malan D, Fulford-Jones T, Welsh M, Moulton S (2004) CodeBlue: an ad hoc sensor network infrastructure for emergency medical care. In: Proceedings of the international workshop on wearable and implantable body sensor networks. http://www.eecs.harvard.edu/mdw/papers/codeblue-bsn04.pdf – reference: Gill M, Little R, Spriggs A, Allen J, Argomaniz J, Waples S (2005) Assessing the impact of CCTV: the Hawkeye case study. Home Office Online Report. http://www.homeoffice.gov.uk/rds/pdfs05/rdsolr1205.pdf – reference: PahlavanKKrishnamurthyPPrinciples of wireless networks: a unified approach2001NJ, USAPrentice Hall PTR, Upper Saddle River – reference: Bahl P, Padmanabhan VN (2000) RADAR: An in-building RF-based user location and tracking system. In: Proceedings of the 19th annual joint conference of the IEEE computer and communications societies. Tel Aviv, Israel. http://www.ieee-infocom.org/2000/papers/589.pdf – reference: GuptaPKumarPRThe capacity of wireless networksIEEE Trans Inform Theory20004623884040991.9051110.1109/18.8257991748976 – reference: RFID-assisted localization and communication for first responders. NIST. http://www.antd.nist.gov/wctg/RFID/RFIDassist.htm – reference: Havas M (2007) Analysis of health and environmental effects of proposed san francisco earthlink wi-fi network – reference: Addlesee M, Curwen R, Hodges S, Newman J, Steggles P, Ward A, Hopper A (2001) Implementing a sentient computing system. IEEE Comput 34(8):http://www.cl.cam.ac.uk/Research/DTG/attarchive/abstracts.html – reference: Akella A., Judd G., Seshan S., Steenkiste P (2005) Self-management in chaotic wireless deployments. In: Proceedings of the 11th annual international conference on mobile computing and networking. Cologne, Germany, pp 185–199. doi:10.1145/1080829.1080849 – reference: Capacity, coverage, and deployment considerations in IEEE 802.11g (2003) White Paper, Cisco Systems – reference: Rahimi M, Baer R, Iroezi OI, Garcia JC, Warrior J, Estrin D, Srivastava M (2005) Cyclops: in situ image sensing and interpretation in wireless sensor networks. In: SenSys ’05: proceedings of the 3rd international conference on Embedded networked sensor systems. ACM, New York, NY, USA, pp 192–204. doi:10.1145/1098918.1098939 – reference: Licandro F, Schembra G (2007) Wireless mesh networks to support video surveillance: architecture, protocol, and implementation issues. EURASIP J Wireless Commun Network 2007(1). doi:10.1155/2007/31976 – reference: Sheng Y, Chen G, Tan K, Deshpande U, Vance B, Yin H, McDonald C, Henderson T, Kotz D, Campbell A, Wright J (2008) MAP: ascalable monitoring system for dependable 802.11 wireless networks. IEEE wireless communications, special issue on dependability issues with ubiquitous wireless access. http://www.cs.uml.edu/glchen/papers/map-ieeewc08.pdf – reference: Kyasanur P, Vaidya NH (2005) Capacity of multi-channel wireless networks: impact of number of channels and interfaces. In: Proceedings of the 11th annual international conference on mobile computing and networking. 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Snippet | Camera-based surveillance system is an important tool for assistive environment to monitor those who may have physical or cognitive impairment. It is, however,... |
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SubjectTerms | Algorithms Assisted living facilities Backbone Cameras Computer Science Feasibility studies Mobile Computing Monitoring Monitoring systems Monitors Networks Original Article Personal Computing Surveillance systems User Interfaces and Human Computer Interaction Wireless communication Wireless networks |
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Title | Design and implementation of a sensor-based wireless camera system for continuous monitoring in assistive environments |
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