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Feedback-Based Parameterized Strategies for Improving Performance of Video Surveillance Understanding Frameworks

  • Nuria Sánchez
  • Noa García
  • José Manuel Menéndez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8864)

Abstract

One of the most ambitious objectives for the Computer Vision research community is to achieve for machines similar capacities to the human’s visual and cognitive system, and thus provide a trustworthy description of what is happening in the scene under surveillance. Most of hierarchic and intelligent video-based understanding frameworks proposed so far allow the development of systems with necessary perception, interpretation and learning capabilities to extract knowledge from a broad set of scenarios, having in common the one-way sequential structure of the functional processing units that compose the system. However, only in a limited number of works, once visual evidence is achieved, feedback is provided within the system to improve system’s performance in any sense. With this motivation, a methodology for introducing feedback in perceptual systems is proposed. Experimental results demonstrate how different parameterized strategies let the system overcome limitations mainly due to sudden changes in the environmental conditions.

Keywords

Feedback Scene understanding Visual interpretation Knowledge representation Framework 

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References

  1. 1.
    Kumar, P., Ranganath, S., Huang, W., Sengupta, K.: Framework for real-time behavior interpretation from traffic video. IEEE Transactions on Intelligent Transportation Systems 6(1), 43–53 (2005)CrossRefGoogle Scholar
  2. 2.
    Velastin, S.A., Boghossian, B.A., Lo, B.P.L., Sun, J., Vicencio-Silva, M.A.: PRISMATICA: toward ambient intelligence in public transport environments. IEEE Trans. Syst. Man Cybern. Part A 35(1), 164–182 (2005)Google Scholar
  3. 3.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 34(3), 334–352 (2004)CrossRefGoogle Scholar
  4. 4.
    Sánchez, N., Menéndez, J.M.: Video Analysis Architecture For Enhancing Pedestrian And Driver Safety In Public Environments. In: 10th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2009), London (2009)Google Scholar
  5. 5.
    Tian, Y., Brown, L., Hampapur, A., Lu, M., Senior, A., Shu, C.: IBM smart surveillance system (S3): event based video surveillance system with an open and extensible framework. Machine Vision and Applications 19(5), 315–327 (2008)zbMATHCrossRefGoogle Scholar
  6. 6.
    Oliver, N., Rosario, B., Pentland, A.P.: A Bayesian computer vision system for modelling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 831–843 (2000)CrossRefGoogle Scholar
  7. 7.
    Mirmehdi, M., Palmer, P.L., Kittler, J., Dabis, H.: Complex feedback strategies for hypothesis generation and verification. In: BMVC, pp. 1–10 (1996)Google Scholar
  8. 8.
    Hall, D.: Automatic parameter regulation of perceptual systems. Image and Vision Computing 24(8), 870–881 (2006)CrossRefGoogle Scholar
  9. 9.
    Kim, J.: Improved Vehicle Detection Method Using Feedback-AdaBoost Learning. International Journal of Computer Theory & Engineering 5(1) (2013)Google Scholar
  10. 10.
    Wang, J., Bebis, G., Nicolescu, M., Nicolescu, M., Miller, R.: Improving Target Detection by Coupling It with Tracking. Machine Vision and Applications 20(4), 205–223 (2009)zbMATHCrossRefGoogle Scholar
  11. 11.
    García, A., Bescós, J.: Video Object Segmentation Based on Feedback Schemes Guided by a Low-Level Scene Ontology. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 322–333. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Bravo, C., Sánchez, N., García, N., Menéndez, J.M.: Outdoor Vacant Parking Space Detector for Improving Mobility in Smart Cities. In: Reis, L.P., Correia, L., Cascalho, J. (eds.) EPIA 2013. LNCS, vol. 8154, pp. 30–41. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Pecharromán, A., Sánchez, N., Torres, J., Menéndez, J.M.: Real-Time Incidents Detection in the Highways of the Future. In: 15th Portuguese Conference on Artificial Intelligence, EPIA 2011, pp. 108–121, Lisbon, Portugal, October 10-13 (2011)Google Scholar
  14. 14.
    Renouf, A., Clouard, R., Revenu, M.: A platform dedicated to knowledge engineering for the development of image processing applications In: AIDSS, pp. 271–276 (2007)Google Scholar
  15. 15.
    Carmona, E.J., Rincón, M., Bachiller, M., Martínez-Cantos, J., Martínez-Tomás, R., Mira, J.: On the effect of feedback in multilevel representation spaces for visual surveillance tasks. Neurocomputing 72(4), 916–927 (2009)CrossRefGoogle Scholar
  16. 16.
    Han, H., Shan, S., Chen, X., Gao, W.: A comparative study on illumination preprocessing in face recognition. Pattern Recognition 46(6), 1691–1699 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nuria Sánchez
    • 1
  • Noa García
    • 1
  • José Manuel Menéndez
    • 1
  1. 1.Grupo de Aplicación de Telecomunicaciones Visuales. ETSI. TelecomunicaciónUniversidad Politécnica de MadridMadridSpain

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