Skip to main content
SpringerLink
Log in

Abnormal High-Level Event Recognition in Parking lot

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 736))

Abstract

In this paper, we presented an approach to automatically detect abnormal high-level events in a parking lot. A high-level event or a scenario is a combination of simple events with spatial, temporal and logical relations. We proposed to define the simple events through a spatio-temporal analysis of features extracted from a low-level processing. The low level processing involves detecting, tracking and classifying moving objects. To naturally model the relations between simpler events, a Petri Nets model was used. The experimental results based on recorded parking video data sets and public data sets illustrate the performance of our approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://vcipl-okstate.org/pbvs/bench/.

  2. 2.

    http://www.ino.ca/Video-Analytics-Dataset.

References

  1. Kim, I.S., Choi, H.S., Yi, K.M., et al.: Intelligent visual surveillance a survey. Int. J. Control Autom. Syst. 8(5), 926–939 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Cheng, Y.H., Wang, J.: A motion image detection method based on the inter-frame difference method. Appl. Mech. Mater. 490–491, 1283–1286 (2014)

    Google Scholar 

  4. Jian-Ping, T., Xiao-lan, L., Jun, L.: Moving object detection and identification method based on vision. Int. J. Secur. Appl. 10(3), 101–110 (2016)

    Google Scholar 

  5. Hariyono, J., Hoang, V.D., Jo, K.H.: Motion segmentation using optical flow for pedestrian detection from moving vehicle. In: Hwang, D., Jung, J.J., Nguyen, N.T. (eds.) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. LNCS, vol. 8733. Springer, Cham (2014)

    Google Scholar 

  6. Kushwaha, A.K.S., Srivastava, S., Srivastava, R.: Multi-view human activity recognition based on silhouette and uniform rotation invariant local binary patterns. Multimed. Syst. J. 32(4), 451–467 (2017)

    Article  Google Scholar 

  7. Gargi, P., Rajbabu, V.: Mean LBP and modified fuzzy C-means weighted hybrid feature for illumination invariant meanshift tracking. Signal Image Video Process. 11(4), 665–672 (2017)

    Article  Google Scholar 

  8. Yang, Y., Cao, Q.: A fast feature points-based object tracking method for robot grasp. Int. J. Adv. Robot. Syst. 10(3) (2013)

    Google Scholar 

  9. Dedeoğlu, Y., Töreyin, B.U., Güdükbay, U., Çetin, A.E.: Silhouette-based method for object classification and human action recognition in video. In: Huang, T.S., et al. (eds.) Computer Vision in Human-Computer Interaction. ECCV 2006. LNCS, vol. 3979, pp. 64–77. Springer, Heidelberg (2006)

    Google Scholar 

  10. Moctezuma, D., Conde, C., Diego, I.M., Cabello, E.: Person detection in surveillance environment with HoGG: Gabor filters and histogram of oriented gradient. In: ICCV Workshops, pp. 1793–1800 (2011)

    Google Scholar 

  11. Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recognit. 36(3), 585–601 (2003)

    Article  Google Scholar 

  12. Zhang, Z., Cai, Y., Huang, K., Tan, T.: Real-time moving object classification with automatic scene division. In: IEEE International Conference on Image Processing, pp. 149–152 (2007)

    Google Scholar 

  13. Chakraborty, B., Bagdanov, A.D., Gonzàlez, J., Roca, X.: Human action recognition using an ensemble of body-part detectors. Expert Syst. 30(2), 101–114 (2012)

    Article  Google Scholar 

  14. Bobick, F., Davis, W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001)

    Article  Google Scholar 

  15. Wang, H., Kläser, A., Schmid, C., Cheng-Lin, L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013)

    Article  MathSciNet  Google Scholar 

  16. Lv, F., Nevatia, R.: Single view human action recognition using key pose matching and Viterbi path searching. In: IEEE Conference on Computer Vision and Pattern Recognition, p. 18 (2007)

    Google Scholar 

  17. Wang, Y., Cao, K: A proactive complex event processing method for large-scale transportation Internet of Things. Int. J. Distrib. Sens. Netw. 10(3) (2014)

    Google Scholar 

  18. Bansal, N.K., Feng, X., Zhang, W., Wei, W., Zhao, Y.: Modeling temporal pattern and event detection using Hidden Markov Model with application to a sludge bulking data. Procedia Comput. Sci. 12, 218–223 (2012)

    Article  Google Scholar 

  19. Zhang, Z., Tan, T., Huang, K.: An extended grammar system for learning and recognizing complex visual events. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 240–255 (2011)

    Article  Google Scholar 

  20. Ghanem, N., DeMenthon, D., Doermann, D., Davis, L.: Representation and recognition of events in surveillance video using Petri nets. In: Conference on Computer Vision and Pattern Recognition Workshop, pp. 112–121 (2004)

    Google Scholar 

  21. Boukhriss, R.R., Fendri, E., Hammami, M.: Moving object classification in infrared and visible spectra. In: International Conference on Machine Vision (2016)

    Google Scholar 

  22. Hammami, M., Jarraya, S.K., Ben-Abdallah, H.: On line background modeling for moving object segmentation in dynamic scene. Multim. Tools Appl. 63(3), 899–926 (2013)

    Article  Google Scholar 

  23. Yang, T., Li, S.Z., Pan, Q., Li, J.: Real-time multiple objects tracking with occlusion handling in dynamic scenes. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)

    Google Scholar 

  24. Uijlings, J., Duta, I.C., Sangineto, E., Sebe, N.: Video classification with densely extracted HOG/HOF/MBH features: an evaluation of the accuracy/computational efficiency trade-off. Int. J. Multimed. Inf. Retr. 4(1), 33–44 (2014)

    Article  Google Scholar 

  25. Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence (1981)

    Google Scholar 

  26. Allen, J.F., Ferguson, F.: Actions and events in interval temporal logical. J. Log. Comput. 4(5), 531–579 (1994)

    Article  MATH  Google Scholar 

  27. Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: IEEE International Conference on Computer Vision (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MIRACL Laboratory, ISIMS, University of Sfax, BP 242, 3021, Sakiet Ezzeit Sfax, Tunisia

    Najla Bouarada Ghrab & Rania Rebai Boukhriss

  2. MIRACL Laboratory, FSS, University of Sfax, Road Sokra km 4, BP 802, 3038, Sfax, Tunisia

    Emna Fendri & Mohamed Hammami

Authors
  1. Najla Bouarada Ghrab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rania Rebai Boukhriss
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emna Fendri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed Hammami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Najla Bouarada Ghrab .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bouarada Ghrab, N., Rebai Boukhriss, R., Fendri, E., Hammami, M. (2018). Abnormal High-Level Event Recognition in Parking lot. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-76348-4_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76347-7

  • Online ISBN: 978-3-319-76348-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation