Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Multimedia Systems
Erschienen in: Multimedia Systems 2/2012

01.03.2012 | Regular Paper

Privacy enabled video surveillance using a two state Markov tracking algorithm

verfasst von: Peng Zhang, Tony Thomas, Sabu Emmanuel

Erschienen in: Multimedia Systems | Ausgabe 2/2012

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Nowadays video surveillance systems are widely deployed in many public places. However, the widespread use of video surveillance violates the privacy rights of the people. Many authors have addressed the privacy issues from various points of view. In this paper we propose a novel, on-demand selectively revocable, privacy preserving mechanism. The surveillance video can be tuned to view with complete privacy or by revoking the privacy of any subset of pedestrians while ensuring complete privacy to the remaining pedestrians. We achieve this by tracking the pedestrians using a novel Markov chain algorithm with two hidden states, detecting the head contour of the tracked pedestrians and obscuring their faces using an encryption mechanism. The detected pedestrian face/head is obscured by encrypting with a unique key derived from a master key for the privacy preservation purpose. The performance evaluations on many challenging surveillance scenarios show that the proposed mechanism can effectively and robustly track as well as identify multiple pedestrians and obscure/unobscure their faces/head in real time.
Vorheriger Artikel Intended human object detection for automatically protecting privacy in mobile video surveillance

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Berger, A.M.: Privacy mode for acquisition cameras and camcorders. US Patent 6067399, Sony Corporation, May 2000
2.
Borgefors, G.: Hierarchical chamfer matching: a parametric edge matching algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 10, 849–865 (1998)CrossRef
3.
Boult, T.E.: Pico: privacy through invertible cryptographic obscuration. In: Proceedings of the Computer Vision for Interactive and Intelligent Environments, pp. 27–38 (2005)
4.
Carrillo, P., Kalva, H., Magliveras, S.: Compression independent object encryption for ensuring privacy in video surveillance. In: ICME 2008, Hanover, Germany (2008)
5.
Castelluccia, C., Mykletun, E., Tsudik, G.: Efficient aggregation of encrypted data in wireless sensor networks. In: International Conference on Mobile and Ubiquitous Systems: Networking and Services, 109–117 (2005)
6.
Chen, D., Chang, Y., Yan, R., Yang, J.: Tools for protecting the privacy of specific individuals in video. EURASIP J. Appl. Signal Process. 1, 107–107 (2007)
7.
Collins, R.T.: Mean-shift blob tracking through scale space. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 234–240, Madison, USA, June, 2003
8.
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)CrossRef
9.
Czyz, J., Ristci, B., Macq, B.: A particle filter for joint detection and tracking of color objects. Image Vis. Comput. 25(8), 1271–1281 (2007)CrossRef
10.
Daemen, J., Rijmen, V.: The block cipher Rijindael. In: Proceedings of the Third International Conference on Smart Card Research and Applications, CARDIS98, LNCS, vol. 1820, pp. 277–284 (2000)
11.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893, June 2005
12.
Dufaux, F., Ebrahimi, T.: Scrambling for video surveillance with privacy. In: 2006 Conference on Computer Vision and Pattern Recognition Workshop, NY (2006)
13.
Dufaux, F., Ebrahimi, T.: Scrambling for privacy protection in video surveillance systems. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1168–1174 (2008)CrossRef
14.
Dufaux, F., Ouaret, M., Abdeljaoued, Y., Navarro, A., Vergnenegre, F., Ebrahimi, T.: Privacy enabling technology for video surveillance. In: Image Processing for Military and Security Applications. Proceedings of SPIE, vol. 6250, pp. 1–12 (2006)
15.
Ebrahimi, T., Pereira, F.: The MPEG-4 Book. Prentice-Hall, Englewood Cliffs (2002)
16.
Gaj, K., Chodowiec, P.: Fast implementation and fair comparison of the final candidates for advanced encryption standard using field programmable gate arrays. In: CT-RSA 2001, pp. 84–99 (2001)
17.
Gavrila, D.M., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle. Int. J. Comput. Vis. 30, 1683–1698 (2007)
18.
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of ACM SIGIR, pp. 50–57 (1999)
19.
Hu, W.M., Tan, T.N., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybernet. C: Appl. Rev. 34, 334–352 (2004)CrossRef
20.
Huang, K.S., Trivedi, M.M.: Integrated detection, tracking, and recognition of faces with omnivideo array in intelligent environments. EURASIP J. Image Video Process. 2008 (2008)
21.
Isard, M., Blake, A.: Condensation conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)CrossRef
22.
Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1296–1311 (2003)CrossRef
23.
Jin, Y., Mokhtarian, F.: Data fusion for robust head tracking by particles. In: IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (PETS), pp. 33–40 (2005)
24.
Kitahara, I., Kogure, K., Hagita, N.: Stealth vision for protecting privacy. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 (2004)
25.
Kwon, J., Lee, K.M.: Visual tracking decomposition. In: IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, June 2010
26.
Li, L., Huang, W., Gu, I.Y.H., Tian, Q.: Foreground object detection from videos containing complex background. In: Proceedings of the Eleventh ACM International Conference on Multimedia, pp. 2–10, CA, USA (2003)
27.
Luo, Y., Ye, S., Cheung, S.-C.S.: Anonymous subject identification in privacy-aware video surveillance. In: ICME, pp. 83–88 (2010)
28.
Martin, K., Plataniotis, K.N.: Privacy protected surveillance using secure visual object coding. IEEE Trans. Circuits Syst. Video Technol. 18, 1152–1162 (2008)CrossRef
29.
Newton, E.M., Sweeney, L., Malin, B.: Preserving privacy by de-identifying face images. IEEE Trans. Knowl. Data Eng. 17(2), 232–243 (2005)CrossRef
30.
Martinez-Ponte, Desurmont, X., Meesen, J., Delaigle, J.-F.: Robust human face hiding ensuring privacy. In: Proceedings of International Workshop on Image Analysis for Multimedia Interactive Services (2005)
31.
Qureshi, F.Z.: Object-video streams for preserving privacy in video surveillance. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (2009)
32.
Robshaw, M.J.B.: Stream Ciphers Technical Report TR-701, Version 2.0. RSA Laboratories (1995)
33.
Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)CrossRef
34.
Schiff, J., Meingast, M., Mulligan, D., Sastry, S., Goldberg, K.: Respectful cameras: detecting visual markers in real-time to address privacy concerns. In: International Conference on Intelligent Robots and Systems, 2007, IROS 2007, pp. 971–978 (2007)
35.
Senior, A., Pankanti, S., Hampapur, A., Brown, L., Tian, Y.-L., Ekin, A., Connell, J., Shu, C.F., Lu, M.: Enabling video privacy through computer vision. IEEE Security Privacy 3(3), 50–57 (2005)CrossRef
36.
Sohn, H., AnzaKu, E.T., Neve, W.D., Ro, Y.M., Plataniotis, K.N.: Privacy protection in video surveillance systems using scalable video coding. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (2009)
37.
Sohn, H., Neve, W.D., Ro, Y.M.: Privacy protection in video surveillance systems: analysis of subband-adaptive scrambling in JPEG XR. In: IEEE Transactions on Circuits and Systems for Video Technology, vol. 99 (2011)
38.
Stanley, J., Steinhardt, B.: Bigger Monster, Weaker Chains: The Growth of an American Surveillance Society. ACLU, New York (2003)
39.
Tansuriyavong, S., Hanaki, S.-I.: Privacy protection by concealing persons in circumstantial video image. In: Proceedings of the Workshop on Perceptive User Interfaces, pp. 1–4 (2001)
40.
Venkatesh, M.V., Cheung, S.-C., Zhao, J.: Efficient object based video inpainting. Pattern Recognit. Lett. 30(2), 168–179 (2009)CrossRef
41.
Wada, J., Kaiyama, K., Ikoma, K., Kogane, H.: Monitor camera system and method of displaying picture from monitor camera thereof. European patent, EP 1081955 A2, Matsushita, April 2001
42.
Wang, J.-G., Suwandy, A., Yau, W.-Y.: Face obscuration in a video sequence by integrating kernel-based mean-shift and active contour. In: 10th Intl. Conf. on Control, Automation, Robotics and Vision (2008)
43.
Wickramasuriya, J., Datt, M., Mehrotra, S., Venkatasubramanian, N.: Privacy protecting data collection in media spaces. ACM International Conference on Multimedia, pp. 48–55 (2004)
44.
Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38, 13–58 (2006)CrossRef
45.
Yu, X., Babaguchi, N.: Privacy preserving: hiding a face in a face. In: Proceedings of ACCV, LNCS, vol. 4844, pp. 651–661 (2007)
46.
Zhang, W., Cheung, S.C., Chen, M.: Hiding Privacy information in video surveillance system. Proceedings of ICIP, vol. 3, pp. 868–871 (2005)
47.
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. 73(2), 213–238 (2007)CrossRef
48.
Zhang, P., Thomas, T., Emmanuel, S., Kankanhalli, M.S.: Privacy preserving video surveillance using pedestrian tracking mechanism. In: Proceedings of the 2nd ACM workshop on Multimedia in Forensics, Security and Intelligence, MiFor2010, Florence, Oct 2010
49.
Zhao, J., Cheung, S.: Multi-camera surveillance with visual tagging and generic camera placement. In: Proceedings of ACM/IEEE International Conference on Distributed Smart Cameras (2007)
50.
Zhu, K.C.Q., Yeh, M.C., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. IEEE Conf. Comput. Vis. Pattern Recognit. 2, 1491–1498 (2006)
51.
Zou, W., Li, Y., Yuan, K., Xu, D.: Real-time elliptical head contour detection under arbitrary pose and wide distance range. J. Vis. Commun. Image Represent. 20, 217–228 (2009)CrossRef
Metadaten
Titel
Privacy enabled video surveillance using a two state Markov tracking algorithm
verfasst von
Peng Zhang
Tony Thomas
Sabu Emmanuel
Publikationsdatum
01.03.2012
Verlag
Springer-Verlag
Erschienen in
Multimedia Systems / Ausgabe 2/2012
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-011-0247-8

Weitere Artikel der Ausgabe 2/2012

Multimedia Systems 2/2012 Zur Ausgabe

Regular Paper

Intended human object detection for automatically protecting privacy in mobile video surveillance

Regular Paper

A general framework for managing and processing live video data with privacy protection

Regular Paper

User-centric privacy awareness in video surveillance

Regular Paper

Chaos-cryptography based privacy preservation technique for video surveillance

Editorial

Guest editorial: Privacy-aware multimedia surveillance systems

Neuer Inhalt

    Bildnachweise