skip to main content
10.1145/2659021.2659049acmconferencesArticle/Chapter ViewAbstractPublication PagesicdscConference Proceedingsconference-collections
tutorial

Self-Coordinated Target Assignment and Camera Handoff in Distributed Network of Embedded Smart Cameras

Published: 04 November 2014 Publication History

Abstract

Tracking several objects across multiple cameras is essential for collaborative monitoring in distributed camera networks. The tractability of the related optimization aiming at tracking a maximal number of important targets, decreases with the growing number of objects moving across cameras. To tackle this issue, a viable model and sound object representation, which can leverage the power of existing tool at run-time for a fast computation of solution, is required.
In this paper, we provide a formalism to object tracking across multiple cameras. A first assignment of objects to cameras is performed at start-up to initialize a set of distributed trackers in embedded cameras. We model the run-time self-coordination problem with target handover by encoding the problem as a run-time binding of objects to cameras. This approach has successively been used in high-level system synthesis. Our model of distributed tracking is based on Answer Set Programming, a declarative programming paradigm, that helps formulate the distribution and target handover problem as a search problem, such that by using existing answer set solvers, we produce stable solutions in real-time by incrementally solving time-based encoded ASP problems. The effectiveness of the proposed approach is proven on a 3-node camera network deployment.

References

[1]
M. Borkar, V. Cevher, and J. H. McClellan. Estimating target state distributions in a distributed sensor network using a monte-carlo approach. In IEEE MLSP 2005, Connecticut, 28--30 September 2005.
[2]
F. Castanedo, J. Garcia, M. A. Patricio, and J. M. Molina. Data fusion to improve trajectory tracking in a cooperative surveillance multi-agent architecture. Information Fusion, 11(3):243 -- 255, 2010.
[3]
E. Coban, F. Ture, and E. Erdem. Comparing asp, cp, ilp on two challenging applications: Wire routing and haplotype inference. In Proc. of LaSh, 2008.
[4]
Ishebabi, H., Mahr, P. and Bobda, C. Automatic Synthesis of Multiprocessor Systems From Parallel Programs under Preemptive Scheduling. In International Conference on ReConFigurable Computing and FPGAs, Mexico, December 2008.
[5]
A. Karimaa. Efficient video surveillance: Performance evaluation in distributed video surveillance systems. 2011.
[6]
M. Kushwaha and X. Koutsoukos. 3d target tracking in distributed smart camera networks with in-network aggregation. In Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC '10, pages 25--32, NY, USA, 2010.
[7]
V. Lifschitz. Answer set programming and plan generation. ARTIFICIAL INTELLIGENCE, 2002.
[8]
A. Manzanera and J. Richefeu. A new motion detection algorithm based on Sigma-Delta background estimation. Pattern Recognition Letters, 28(3):320--328, 2007.
[9]
L. Marchesotti, S. Piva, and C. Regazzoni. An agent-based approach for tracking people in indoor complex environments. Image Analysis and Processing, International Conference on, 0:99, 2003.
[10]
M. Mefenza, F. Yonga, and C. Bobda. Razorcam: A prototyping environment for video communication. International Workshop on Mobile Computing Systems and Applications, February 2013.
[11]
N. T. Nguyen, S. Venkatesh, G. West, and H. H. Bui. Multiple camera coordination in a surveillance system. ACTA Automatica Sinica, 29:408--422, 2003.
[12]
M. Nogueira, M. Balduccini, M. Gelfond, R. Watson, and M. Barry. An a-prolog decision support system for the space shuttle. In In PADL 2001, pages 169--183. Springer, 2000.
[13]
M. Quaritsch, M. Kreuzthaler, B. Rinner, H. Bischof, and B. Strobl. Autonomous multicamera tracking on embedded smart cameras. EURASIP J. Emb. Sys., 2007.
[14]
B. Rinner and M. Quaritsch. Embedded middleware for smart camera networks and sensor fusion. Elsevir, 2008.
[15]
A. Rowe, C. Rosenberg, and I. Nourbakhsh. A second generation low cost embedded color vision system. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, page 136, 2005.
[16]
University of Potsdam. Potassco - Tools for Answer Set Programming. http://potassco.sourceforge.net/, 2010.
[17]
S. Velipasalar, J. Schlessman, C.-Y. Chen, W. Wolf, and J. Singh. Sccs: A scalable clustered camera system for multiple object tracking communicating via message passing interface. pages 277--280. IEEE, 2006.

Cited By

View all
  • (2019)Distributed Video Surveillance Using Smart CamerasJournal of Grid Computing10.1007/s10723-018-9467-x17:1(59-77)Online publication date: 1-Mar-2019
  • (2017)A prediction-based distributed tracking protocol for video surveillance2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)10.1109/ICNSC.2017.8000081(140-145)Online publication date: May-2017
  • (2016)Efficient network clustering for traffic reduction in embedded smart camera networksJournal of Real-Time Image Processing10.1007/s11554-015-0498-212:4(813-826)Online publication date: 1-Dec-2016
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
ICDSC '14: Proceedings of the International Conference on Distributed Smart Cameras
November 2014
286 pages
ISBN:9781450329255
DOI:10.1145/2659021
  • General Chair:
  • Andrea Prati,
  • Publications Chair:
  • Niki Martinel
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

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2014

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Answer Set Programming
  2. Distributed Network
  3. FPGA
  4. Smart Embedded Camera
  5. Target Tracking

Qualifiers

  • Tutorial
  • Research
  • Refereed limited

Conference

ICDSC '14
Sponsor:

Acceptance Rates

ICDSC '14 Paper Acceptance Rate 49 of 69 submissions, 71%;
Overall Acceptance Rate 92 of 117 submissions, 79%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 18 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2019)Distributed Video Surveillance Using Smart CamerasJournal of Grid Computing10.1007/s10723-018-9467-x17:1(59-77)Online publication date: 1-Mar-2019
  • (2017)A prediction-based distributed tracking protocol for video surveillance2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)10.1109/ICNSC.2017.8000081(140-145)Online publication date: May-2017
  • (2016)Efficient network clustering for traffic reduction in embedded smart camera networksJournal of Real-Time Image Processing10.1007/s11554-015-0498-212:4(813-826)Online publication date: 1-Dec-2016
  • (2015)DICE: A Distributed Protocol for Camera-Aided Video Surveillance2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing10.1109/CIT/IUCC/DASC/PICOM.2015.68(477-484)Online publication date: Oct-2015

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media