Skip to main content
SpringerLink
Log in

Classification of Abandoned and Unattended Objects, Identification of Their Owner with Threat Assessment for Visual Surveillance

  • Conference paper
  • First Online:
Proceedings of 3rd International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1022))

  • 753 Accesses

Abstract

Terrorism is on an ever-increasing rise and is one of the major threats the world is facing today. Terrorist attacks mostly take place in crowded areas such as railway stations and airports. They involve the use of explosives which are placed inside suspicious abandoned objects like bags, suitcases, etc. In this paper, we are proposing a model that can classify abandoned and unattended objects separately and backtrack to identify the owner as well as find the last known location of the owner in a social environment using visual surveillance feed in real time for rapid alert and action.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

References

  1. Gurung, S.: India witness highest bomb blasts in world in past two years (2018). https://economictimes.indiatimes.com/news/defence/india-witnessed-highest-number-of-bomb-blasts-in-world-in-past-two-years/articleshow/57082541.cms

  2. Video Surveillance Market by System, Offering, Vertical, and Geography—Global Forecast to 2023. https://www.marketsandmarkets.com/Market-Reports/video-surveillance-market-645.html

  3. Zivkovic, Z., Van Der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)

    Article  Google Scholar 

  4. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer, Cham (2016)

    Google Scholar 

  5. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Proceedings of CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  6. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  7. Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: VISAPP (1), vol. 2(331–340), p. 2 (2009)

    Google Scholar 

  8. Bishop, G., Welch, G.: An introduction to the Kalman filter. In: Proceedings of SIGGRAPH, Course, vol. 8(27599-3175), p. 59 (2001)

    Google Scholar 

  9. Baur, R., Efros, A., Hebert, M.: Statistics of 3d object locations in images (2008)

    Google Scholar 

  10. In: Ninth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance 2006, PETS 2006 dataset. http://www.cvg.reading.ac.uk/PETS2006/data.html

  11. i-Lids dataset for AVSS 2007. http://www.eecs.qmul.ac.uk/~andrea/avss2007_d.html

  12. Pan, J., Fan, Q., Pankanti, S.: Robust abandoned object detection using region-level analysis. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 3597–3600. IEEE (2011)

    Google Scholar 

  13. Li, L., Luo, R., Ma, R., Huang, W., Leman, K.: Evaluation of an IVS system for abandoned object detection on PETS 2006 datasets. In: Proceedings of the IEEE Workshop PETS, pp. 91–98 (2006)

    Google Scholar 

  14. Tian, Y., Feris, R.S., Liu, H., Hampapur, A., Sun, M.T.: Robust detection of abandoned and removed objects in complex surveillance videos. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41(5), 565–576 (2011)

    Google Scholar 

  15. Porikli, F., Ivanov, Y., Haga, T.: Robust abandoned object detection using dual foregrounds. EURASIP J. Adv. Signal Process. 2008, 30 (2008)

    MATH  Google Scholar 

  16. Liao, H.H., Chang, J.Y., Chen, L.G.: A localized approach to abandoned luggage detection with foreground-mask sampling. In: IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, 2008, AVSS’08, pp. 132–139. IEEE (2008)

    Google Scholar 

  17. Evangelio, R.H., Senst, T., Sikora, T.: Detection of static objects for the task of video surveillance. In: 2011 IEEE Workshop on Applications of Computer Vision (WACV), pp. 534–540. IEEE (2011)

    Google Scholar 

Download references

Acknowledgements

We thank Mr. Anuj Khare, Mr. Chinmay Swaroop Saini, and Mr. Harshit Choubey (Undergraduate Students at IIITDM Jabalpur) for helping us in creating our own dataset. The videos in the dataset were shot at IIITDM Jabalpur campus not violating any ethical obligation and feature the above mentioned volunteers and authors with their consent.

Author information

Authors and Affiliations

  1. PDPM Indian Institute of Information Technology Design & Manufacturing Jabalpur, Jabalpur, India

    Harsh Agarwal, Gursimar Singh & Mohammed Arshad Siddiqui

Authors
  1. Harsh Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gursimar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Arshad Siddiqui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gursimar Singh .

Editor information

Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. Department of Advanced Information Technology and Computer Sciences, Tokyo Institute of Agriculture and Technology, Koganei, Tokyo, Japan

    Masaki Nakagawa

  3. Department of Computer Science, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Jabalpur, Madhya Pradesh, India

    Pritee Khanna

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Agarwal, H., Singh, G., Siddiqui, M.A. (2020). Classification of Abandoned and Unattended Objects, Identification of Their Owner with Threat Assessment for Visual Surveillance. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-32-9088-4_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9087-7

  • Online ISBN: 978-981-32-9088-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation