article

Time-based feedback-control framework for real-time video surveillance systems with utilization control

Authors:

Seyed Ahmad Motamedi,

Saeed SharifianAuthors Info & Claims

Journal of Real-Time Image Processing, Volume 16, Issue 4

Pages 1301 - 1316

https://doi.org/10.1007/s11554-016-0637-4

Published: 01 August 2019 Publication History

Abstract

Multi-camera surveillance systems generally are used by intelligent vision applications to analysis moving objects in the urban environments. Object tracking is an essential component of these applications. Real-time tracking of moving objects in the crowded urban scenes is a challenging problem because the number of moving objects in the scene varies and is usually unpredictable. In order to meet the real-time requirements of intelligent vision applications, it is necessary to control the surveillance workload over processing system and enforce the utilization bound on the system. The utilization control is challenging especially when the workload in the system is unpredictable. The workload for a particular camera depending on the number of targets in its view and execution rate of services that are used to detect and track the moving objects. In this paper, an adaptive real-time system based on the feedback-control framework is proposed to control the system utilization by adjusting input video frame rate that guarantees real-time performance and quality of intelligent vision applications. This approach can handle the workload uncertainties by identifying the parameters of system model online by solving an optimization problem and can simultaneously adjust the frame rate in each sampling period. In each sampling instance, the system utilization is monitored and the error is obtained by comparing it with the set point. The real-time operation of the computing system is achieved by adjusting the input video frame rate to keep the utilization at a given set point slightly below their schedulable bound. Evaluation results demonstrate proposed scheme outperforms the existing schemes especially under workload uncertainties.

References

[1]

Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recogn. Lett. 34(1), 3---19 (2013)

Digital Library

[2]

Song, B., Ding, C., Kamal, A.T., Farrell, J.A., Roy-Chowdhury, A.K.: Distributed camera networks. IEEE Signal Proc. Mag. 28(3), 20---31 (2012)

[3]

Wang, G., Tao, L., Di, H., Ye, X., Shi, Y.: A scalable distributed architecture for intelligent vision system. IEEE Trans. Ind. Inform. 8(1), 91---99 (2012)

[4]

Xu, R., Guan, Y., Huang, Y.: Multiple human detection and tracking based on head detection for real-time video surveillance. Multimed. Tools Appl. 74(3), 729---742 (2015)

Digital Library

[5]

Kumar, P., Pande, A., Mittal, A.: Efficient compression and network adaptive video coding for distributed video surveillance. Multimed. Tools Appl. 56(2), 365---384 (2012)

Digital Library

[6]

Lim, M.K., Tang, S., Chan, C.S.: iSurveillance: intelligent framework for multiple events detection in surveillance videos. Expert Syst. Appl. 41(10), 4704---4715 (2014)

[7]

Maurin, B., Masoud, O., Papanikolopoulos, N.P.: Tracking all traffic: computer vision algorithms for monitoring vehicles, individuals, and crowds. IEEE Robot Autom. Mag. 12(1), 29---36 (2005)

[8]

Georis, B., Bremond, F., Thonnat, M.: Real-time control of video surveillance systems with program supervision techniques. Mach. Vis. Appl. 18(3---4), 189---205 (2007)

[9]

Sarkar, R., Bakshi, S., Sa, P.K.: A real-time model for multiple human face tracking from low-resolution surveillance videos. In: Proceedings of the 2nd International Conference on Communication, Computing and Security, Procedia Technology, vol. 6, pp. 1004---1010 (2012)

[10]

Lee, C.Y., Lin, S.J., Lee, C.W., Yang, C.S.: An efficient continuous tracking system in real-time surveillance application. J. Netw. Comput. Appl. 35(3), 1067---1073 (2012)

[11]

Huang, D.Y., Chen, C.H., Chen, T.Y., Hu, W.C., Chen, B.C.: Rapid detection of camera tampering and abnormal disturbance for video surveillance system. J. Vis. Commun. Image R. 25(2), 1865---1877 (2014)

Digital Library

[12]

Czyzewski, A., Bratoszewski, P., Ciarkowski, A., Cichowski, J., Lisowski, K., Szczodrak, M., Szwoch, G., Krawczyk, H.: Massive surveillance data processing with supercomputing cluster. Inform. Sci. 296, 322---344 (2015)

Digital Library

[13]

Vishwakarma, S., Agrawal, A.: A survey on activity recognition and behavior understanding in video surveillance. Vis. Comput. 29(10), 983---1009 (2013)

[14]

Handa, A.: Analysing High Frame rate Camera Tracking. Submitted in part fulfillment of the requirements for the degree of Ph.D. in Computing and the Diploma of Imperial College London (2013)

[15]

Sha, L., Abdelzaher, T., Arzen, K.E., Cervin, A., Baker, T., et al.: Real time scheduling theory: a historical perspective. Realt. Syst. 28(2---3), 101---155 (2004)

[16]

Zhang, F., Burns, A.: Schedulability analysis for real-time systems with EDF scheduling. IEEE Trans. Comput. 58(9), 1250---1258 (2009)

Digital Library

[17]

Teng, F.: Ressource Allocation and Schelduling Models for Cloud Computing. Other. Ecole Centrale Paris, 2011. English. . (2011)

[18]

Vankeirsbilck, B., Verslype, D., Staelens, N., Simoens, P., Develder, C., et al.: Platform for real-time subjective assessment of interactive multimedia applications. Multimed. Tools Appl. 72(1), 749---775 (2014)

Digital Library

[19]

Saini, M., Wang, X., Atrey, P.K., Kankanhalli, M.: Adaptive workload equalization in multi-camera surveillance systems. IEEE Trans. Multimed. 14(3), 555---562 (2012)

Digital Library

[20]

Wang, X., Chen, Y., Lu, C., Koutsoukos, X.D.: Towards controllable distributed real-time systems with feasible utilization control. IEEE Trans. Comput. 58(8), 1095---1110 (2009)

Digital Library

[21]

Wang, X., Lu, C., Gill, C.: FCS/nORB: a feedback control real-time scheduling service for embedded ORB middleware. Microprocess. Microsyt. 32(8), 413---424 (2008)

Digital Library

[22]

Hossain, M.S.: QoS-aware service composition for distributed video surveillance. Multimed. Tools Appl. 73(1), 169---188 (2014)

Digital Library

[23]

Zhu, W., Luo, C., Wang, J., Li, S.: Multimedia cloud computing. IEEE Signal Proc. Mag. 28(3), 59---69 (2011)

[24]

Cooharojananone, N., Kasamwattanarote, S., Lipikorn, R., Satoh, S.: Automated real-time video surveillance summarization. J Realt. Image Proc. (2012).

[25]

Jianjun, L., Ming, X., Lee, V.C.S., LihChyun, S., Guohui, L.: Workload-efficient deadline and period assignment for maintaining temporal consistency under EDF. IEEE Trans. Comput. 62(6), 1255---1268 (2013)

Digital Library

[26]

Abdelzaher, T.F., Sharma, V., Chenyang, L.: A utilization bound for aperiodic tasks and priority driven scheduling. IEEE Trans. Comput. 53(3), 334---350 (2004)

Digital Library

[27]

Weijia, S., Zhen, X., Qi, C., Haipeng, L.: Adaptive resource provisioning for the cloud using online bin packing. IEEE Trans. Comput. 63(11), 2647---2660 (2014)

Digital Library

[28]

Yang, R., Mei, R.D., Roubos, D., Seinstra, F.J., Bal, H.E.: Resource optimization in distributed real-time multimedia applications. Multimed. Tools Appl. 59(3), 941---971 (2012)

Digital Library

[29]

Cha, H., Oh, J., Ha, R.: Dynamic frame dropping for bandwidth control in MPEG streaming system. Multimed. Tools Appl. 19(2), 155---178 (2003)

Digital Library

[30]

Yuen, J.C.H., Chan, E., Lam, K.Y.: Real time video frames allocation in mobile networks using cooperative pre-fetching. Multimed. Tools Appl. 32(3), 329---352 (2006)

Digital Library

[31]

Sun, Y., Feng, Z., Ginnavaram, R.R.: A direct nonbuffer rate control algorithm for real time video compression. Multimed. Tools Appl. (2014).

[32]

Bellemans, T., Schutter, B.D., Moor, B.D.: Models for traffic control. Control systems engineering. J. A 43(3---4), 13---22 (2002)

[33]

Markowski, M.J.: Modeling Behavior in Vehicular and Pedestrian Traffic Flow. A dissertation submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil Engineering (2008)

[34]

Franklin, G.F., Powell, J.D., Workman, M.: Digital Control of Dynamic Systems. Pearson Education, Third edition (fifth Indian reprint) (2005)

[35]

PETS (2000---2014) Performance Evaluation of Tracking and Surveillance. http://www.pets2014.net

Cited By

Khan NUllah AHaq IMenon VBaik S(2021)SD-Net: Understanding overcrowded scenes in real-time via an efficient dilated convolutional neural networkJournal of Real-Time Image Processing10.1007/s11554-020-01020-818:5(1729-1743)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s11554-020-01020-8
Pashaei AGhatee MSajedi H(2020)Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident imagesJournal of Real-Time Image Processing10.1007/s11554-019-00852-317:4(1051-1066)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1007/s11554-019-00852-3
Liu JWang YLiu ATavares JXu Z(2019)DDS based real-time image monitoring distributed systemProceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing10.1145/3371425.3371485(1-5)Online publication date: 19-Dec-2019
https://dl.acm.org/doi/10.1145/3371425.3371485

Index Terms

Time-based feedback-control framework for real-time video surveillance systems with utilization control
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

Index terms have been assigned to the content through auto-classification.

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cover image Journal of Real-Time Image Processing

Journal of Real-Time Image Processing Volume 16, Issue 4

August 2019

504 pages

ISSN:1861-8200

Issue’s Table of Contents

Copyright © Copyright © 2019 Springer-Verlag GmbH Germany, part of Springer Nature.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 August 2019

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Cited By

Khan NUllah AHaq IMenon VBaik S(2021)SD-Net: Understanding overcrowded scenes in real-time via an efficient dilated convolutional neural networkJournal of Real-Time Image Processing10.1007/s11554-020-01020-818:5(1729-1743)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s11554-020-01020-8
Pashaei AGhatee MSajedi H(2020)Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident imagesJournal of Real-Time Image Processing10.1007/s11554-019-00852-317:4(1051-1066)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1007/s11554-019-00852-3
Liu JWang YLiu ATavares JXu Z(2019)DDS based real-time image monitoring distributed systemProceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing10.1145/3371425.3371485(1-5)Online publication date: 19-Dec-2019
https://dl.acm.org/doi/10.1145/3371425.3371485

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