research-article

Panoptes: servicing multiple applications simultaneously using steerable cameras

Authors:

Marco Gruteser,

Paramvir BahlAuthors Info & Claims

IPSN '17: Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks

Pages 119 - 130

https://doi.org/10.1145/3055031.3055085

Published: 18 April 2017 Publication History

Abstract

Steerable surveillance cameras offer a unique opportunity to support multiple vision applications simultaneously. However, state-of-art camera systems do not support this as they are often limited to one application per camera. We believe that we should break the one-to-one binding between the steerable camera and the application. By doing this we can quickly move the camera to a new view needed to support a different vision application. When done well, the scheduling algorithm can support a larger number of applications over an existing network of surveillance cameras. With this in mind we developed Panoptes, a technique that virtualizes a camera view and presents a different fixed view to different applications. A scheduler uses camera controls to move the camera appropriately providing the expected view for each application in a timely manner, minimizing the impact on application performance. Experiments with a live camera setup demonstrate that Panoptes can support multiple applications, capturing up to 80% more events of interest in a wide scene, compared to a fixed view camera.

References

[1]

Telegraph 2013. One surveillance camera for every 11 people in Britain. https://goo.gl/gXFmwN. (2013).

[2]

BBC 2006. Britain is Surveillance Society. http://news.bbc.co.uk/2/hi/uk/6108496.stm. (2006).

[3]

Nest Cam 2013. Nest Cam. https://goo.gl/I85twa. (2013).

[4]

Cash J. Costello, Christopher P. Diehl, Amit Banerjee, and Hesky Fisher. 2004. Scheduling an Active Camera to Observe People. In Proceedings of the ACM 2Nd International Workshop on Video Surveillance & Sensor Networks (VSSN '04). ACM, New York, NY, USA, 39--45.

Digital Library

[5]

J. C. Neves and H. Proena. 2015. Dynamic camera scheduling for visual surveillance in crowded scenes using Markov random fields. In Advanced Video and Signal Based Surveillance (AVSS), 2015 12th IEEE International Conference on.

[6]

Adrian Ilie and Greg Welch. 2014. Online Control of Active Camera Networks for Computer Vision Tasks. ACM Trans. Sen. Netw., Article 25 (2014), 40 pages.

Digital Library

[7]

Navin K. Sharma, David E. Irwin, Prashant J. Shenoy, and Michael Zink. 2011. MultiSense: Fine-grained Multiplexing for Steerable Camera Sensor Networks. In Proceedings of the Second Annual ACM Conference on Multimedia Systems (MMSys '11). ACM, New York, NY, USA, 23--34.

Digital Library

[8]

A. J. Lipton, H. Fujiyoshi, and R. S. Patil. 1998. Moving target classification and tracking from real-time video. In IEEE WACV.

Digital Library

[9]

C. Stauffer and W. E. L. Grimson. 1999. Adaptive background mixture models for real-time tracking. In IEEE CVPR.

[10]

Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos. 2010. Anomaly detection in crowded scenes. IEEE CVPR (2010).

[11]

Paul Viola, Michael J. Jones, and Daniel Snow. 2005. Detecting Pedestrians Using Patterns of Motion and Appearance. Int. Journal of Computer Vision 2 (2005).

Digital Library

[12]

Timo Ahonen, Abdenour Hadid, and Matti Pietikäinen. 2004. Face recognition with local binary patterns. In ECCV 2004. Springer.

[13]

Nam T.Nguyen, Svetha Venkatesh, Geoff West, and Hung H. Bui. 2003. Multiple camera coordination in a surveillance system. ACTA Automatica Sinica (2003).

[14]

A. W. Senior, A. Hampapur, and M. Lu. 2005. Acquiring Multi-Scale Images by Pan-Tilt-Zoom Control and Automatic Multi-Camera Calibration. In IEEE WACV/MOTIONS.

Digital Library

[15]

C.J. Costello and I-Jeng Wang. 2005. Surveillance Camera Coordination Through Distributed Scheduling. In CDC-ECC '05.

[16]

K. Nummiaro, E. Koller-Meier, T. Svoboda, D. Roth, and L. Van Gool. 2003. Color-Based Object Tracking in Multi-Camera Environments. Lecture Notes in Comp. Sci. (2003).

[17]

F. Z. Qureshi and D. Terzopoulos. 2009. Planning ahead for PTZ camera assignment and handoff. In Distributed Smart Cameras, 2009. ICDSC 2009. Third ACM/IEEE International Conference on. 1--8.

[18]

Yi Yao, Chung-Hao Chen, Andreas Koschan, and Mongi Abidi. 2010. Adaptive online camera coordination for multi-camera multi-target surveillance. Computer Vision and Image Understanding (2010).

Digital Library

[19]

Chung-Hao Chen, Yi Yao, David Page, Besma Abidi, Andreas Koschan, and Mongi Abidi. 2010. Camera handoff and placement for automated tracking systems with multiple omnidirectional cameras. Computer Vision and Image Understanding (2010).

Digital Library

[20]

Canon 2015. Canon goes resolution-crazy with a 250-megapixel sensor. https://goo.gl/hIKx7N. (2015).

[21]

Onvif 2016. ONVIF. http://www.onvif.org/. (2016).

[22]

Pelco 2016. Pelco SDK. https://goo.gl/pI1CIZ. (2016).

[23]

Onvif 2016. ONVIF Video Specification. https://goo.gl/W6B3nK. (2016).

[24]

E. Bas, A. M. Tekalp, and F. S. Salman. 2007. Automatic Vehicle Counting from Video for Traffic Flow Analysis. In Intelligent Vehicles Symp.

[25]

D. Beymer, P. McLauchlan, B. Coifman, and J. Malik. 1997. A real-time computer vision system for measuring traffic parameters. In CVPR.

Digital Library

[26]

A. B. Chan, Zhang-Sheng John Liang, and N. Vasconcelos. 2008. Privacy preserving crowd monitoring: Counting people without people models or tracking. In CVPR.

[27]

Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, and Sei-Wan Chen. 2004. Automatic license plate recognition. IEEE Trans. Intell. Transp.Syst. 1 (2004), 42--53.

Digital Library

[28]

C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, I. D. Psoroulas, V. Loumos, and E. Kayafas. 2008. License Plate Recognition From Still Images and Video Sequences: A Survey. IEEE Trans. Intell. Transp. Syst. 3 (2008).

Digital Library

[29]

Ashkan Nikravesh, Hongyi Yao, Shichang Xu, David Choffnes, and Z. Morley Mao. 2015. Mobilyzer: An Open Platform for Controllable Mobile Network Measurements (MobiSys). ACM, New York, NY, USA, 16.

Digital Library

[30]

Erik V Cuevas, Daniel Zaldivar, and Raul Rojas. 2005. Kalman filter for vision tracking. (2005).

[31]

R. I. Hartley and A. Zisserman. 2004. Multiple View Geometry in Computer Vision (second ed.). Cambridge University Press.

Digital Library

[32]

Herbert Bay, Tinne Tuytelaars, and Luc Gool. 2006. ECCV 2006. Chapter SURF: Speeded Up Robust Features.

Digital Library

[33]

Pelco 2016. Pelco Spectra. https://goo.gl/M991vQ. (2016).

[34]

OpenCV. 2017. OpenCV Motion Analysis. (2017).

[35]

libvlc 2016. libVLC. https://wiki.videolan.org/LibVLC/. (2016).

[36]

G-NetTrack 2010. G-NetTrack Pro. https://goo.gl/MHEQJ7. (2010).

Cited By

Doan MTran VVan S(2024)Vehicle counting application utilizing background subtraction method with large-scale camera dataEAI Endorsed Transactions on Smart Cities10.4108/eetsc.32117:3Online publication date: 18-Apr-2024
https://doi.org/10.4108/eetsc.3211
Dadheech ABhavsar MVerma JPrasad V(2024)Deep learning based smart traffic management using video analytics and IoT sensor fusionSoft Computing10.1007/s00500-024-10382-128:23-24(13461-13476)Online publication date: 25-Nov-2024
https://doi.org/10.1007/s00500-024-10382-1
Fahim APapalexakis EKrishnamurthy SK. Roy Chowdhury AKaplan LAbdelzaher T(2023)AcTrak: Controlling a Steerable Surveillance Camera using Reinforcement LearningACM Transactions on Cyber-Physical Systems10.1145/35853167:2(1-27)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3585316
Show More Cited By

Index Terms

Panoptes: servicing multiple applications simultaneously using steerable cameras
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensors and actuators
  2. Real-time systems
    1. Real-time system architecture
2. Information systems
  1. Information systems applications
    1. Mobile information processing systems

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Published In

cover image ACM Other conferences

IPSN '17: Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks

April 2017

333 pages

ISBN:9781450348904

DOI:10.1145/3055031

General Chair:
Pei Zhang
Carnegie Mellon University
,
Program Chairs:
Prabal Dutta
University of California, Berkeley
,
Guoliang Xing
Michigan State University

Copyright © 2017 ACM.

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SIGBED: ACM Special Interest Group on Embedded Systems

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New York, NY, United States

Publication History

Published: 18 April 2017

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IPSN '17

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IPSN '17: The 16th International Conference on Information Processing in Sensor Networks

April 18 - 20, 2017

Pennsylvania, Pittsburgh

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Overall Acceptance Rate 143 of 593 submissions, 24%

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Cited By

Doan MTran VVan S(2024)Vehicle counting application utilizing background subtraction method with large-scale camera dataEAI Endorsed Transactions on Smart Cities10.4108/eetsc.32117:3Online publication date: 18-Apr-2024
https://doi.org/10.4108/eetsc.3211
Dadheech ABhavsar MVerma JPrasad V(2024)Deep learning based smart traffic management using video analytics and IoT sensor fusionSoft Computing10.1007/s00500-024-10382-128:23-24(13461-13476)Online publication date: 25-Nov-2024
https://doi.org/10.1007/s00500-024-10382-1
Fahim APapalexakis EKrishnamurthy SK. Roy Chowdhury AKaplan LAbdelzaher T(2023)AcTrak: Controlling a Steerable Surveillance Camera using Reinforcement LearningACM Transactions on Cyber-Physical Systems10.1145/35853167:2(1-27)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3585316
Paul SRao KCoviello GSankaradas MHu YChakradhar S(2023)Elixir: A System to Enhance Data Quality for Multiple Analytics on a Video Stream2023 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP58114.2023.00030(98-107)Online publication date: Jun-2023
https://doi.org/10.1109/SMARTCOMP58114.2023.00030
Tong PLi MLi MHuang JHua X(2021)Large-scale vehicle trajectory reconstruction with camera sensing networkProceedings of the 27th Annual International Conference on Mobile Computing and Networking10.1145/3447993.3448617(188-200)Online publication date: 25-Oct-2021
https://dl.acm.org/doi/10.1145/3447993.3448617
Albusac JVallejo DCastro-Schez JSanchez-Sobrino SGomez-Portes C(2021)Multi-analysis surveillance and dynamic distribution of computational resources: Towards extensible, robust, and efficient monitoring of environmentsExpert Systems with Applications10.1016/j.eswa.2021.114692175(114692)Online publication date: Aug-2021
https://doi.org/10.1016/j.eswa.2021.114692
Nigade VWang LBal H(2020)Clownfish: Edge and Cloud Symbiosis for Video Stream Analytics2020 IEEE/ACM Symposium on Edge Computing (SEC)10.1109/SEC50012.2020.00012(55-69)Online publication date: Nov-2020
https://doi.org/10.1109/SEC50012.2020.00012
Jain SGruteser M(2019)Recognizing Textures with Mobile Cameras for Pedestrian Safety ApplicationsIEEE Transactions on Mobile Computing10.1109/TMC.2018.286865918:8(1911-1923)Online publication date: 1-Aug-2019
https://doi.org/10.1109/TMC.2018.2868659
Liu RSrivastava MKasikci BSilberstein M(2018)VirtSenseProceedings of the 3rd Workshop on System Software for Trusted Execution10.1145/3268935.3268937(2-7)Online publication date: 15-Oct-2018
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Bhardwaj RTummala GRamalingam GRamjee RSinha P(2018)AutoCalibACM Transactions on Sensor Networks10.1145/319966714:3-4(1-27)Online publication date: 27-Nov-2018
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