Skip to main content
SpringerLink
Log in
Book cover

Advances in Distributed Computing and Machine Learning pp 247–256Cite as

Road Accident Detection and Severity Determination from CCTV Surveillance

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 127))

Abstract

Wide variety of road types like intersections, highways poses a real challenge to the computer vision algorithms. Hence, there is a need of efficient algorithm to detect the accident on road and also evaluate the severity of the incident. This can be used to improve the emergency services response time. The work demonstrated in this paper aims to develop such an algorithm by modifying existing CCTV surveillance system. In this work, the accident is detected by the dispersion in the motion field of the vehicles during collision. Motion field of the road is obtained from the optical flow of the video frames. The moving objects in the frames are segmented and tracked. The dispersion in the angle vector of the optical flow is derived for each of the moving object. The dispersion of angle vector for each object is monitored, and deviation of the same from the threshold is determined as an accident. The harshness of the accident can be found by the range of dispersion of the motion field. The algorithm developed here is capable of detecting accidents between any types of moving objects.

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. World Health Organization (2015) Global status report on road safety 2015. World Health Organization

    Google Scholar 

  2. Sánchez-Mangas R, Garcia-Ferrrer A, De Juan A, Arroyo AM (2010) The probability of death in road traffic accidents. How important is a quick medical response? Accident Anal Prevent 42(4):1048–1056

    Google Scholar 

  3. Yun K, Jeong H, Yi KM, Kim SW, Choi JY (2014) Motion interaction field for accident detection in traffic surveillance video. In: 2014 22nd international conference on pattern recognition (ICPR). IEEE, pp 3062–3067

    Google Scholar 

  4. Manocha M, Kaur P (2014) Object tracking techniques for video tracking: a survey. Int J Eng Sci (IJES) 3(6)

    Google Scholar 

  5. Rout RK (2013) A survey on object detection and tracking algorithms. Ph.D. dissertation

    Google Scholar 

  6. Gunnar F (2002) Polynomial expansion for orientation and motion estimation. , Ph. D. dissertation. Linköping University Electronic Press

    Google Scholar 

  7. Maaloul B, Taleb-Ahmed A, Niar S, Harb N, Valderrama C (2017) Adaptive video-based algorithm for accident detection on highways. In: 2017 12th IEEE international symposium on Industrial embedded systems (SIES). IEEE, pp 1–6

    Google Scholar 

  8. Anand R, Veni S, Aravinth J (2016) An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method. In: 2016 international conference on recent trends in information technology (ICRTIT). IEEE

    Google Scholar 

  9. Sabeenian RS et al (2019) Palm-leaf manuscript character recognition and classification using convolutional neural networks. In: Computing and network sustainability. Springer, Singapore, pp 397–404

    Google Scholar 

  10. Harsha SS, Anne KR (2016) A highly robust vehicle detection, tracking and speed measurement model for intelligent transport systems. Int J Appl Eng Res 11(5):3733–3742

    Google Scholar 

  11. Lee IJ (2012) An accident detection system on highway through CCTV with calogero-moser system. In: 18th Asia-Pacific conference on communications (APCC). IEEE, pp 522–525

    Google Scholar 

  12. Sadeky S, Al-Hamadiy A, Michaelisy B, Sayed U (2010) Real-time automatic traffic accident recognition using hfg. In: 2010 20th international conference on pattern recognition (ICPR). IEEE, pp 3348-3351

    Google Scholar 

  13. Ki Y-K (2007) Accident detection system using image processing and MDR. Int J Comput Sci Network Secur (IJCSNS) 7(3):35–39

    Google Scholar 

  14. McCahill M, Norris C (2002) CCTV in London. Report deliverable of UrbanEye project

    Google Scholar 

  15. Kamijo S, Matsushita Y, Ikeuchi K, Sakauchi M (2000) Traffic monitoring and accident detection at intersections. IEEE Trans Intell Transport Syst 1(2):108–118

    Article  Google Scholar 

  16. Akshay S, Thomas S, Ram Prashanth A (2016) Improved multiple object detection and tracking using KF-OF method’. Int J Eng Technol 8:1162-1168

    Google Scholar 

  17. Megalingam RK, Nair RN, Prakhya SM (2010) Wireless vehicular accident detection and reporting system. In: 2010 international conference on mechanical and electrical technology (ICMET 2010), Singapore, pp 636–640

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amrita School of Engineering, Amrita Vishwa Vidhayapeetham, Amrita University, Coimbatore, 641112, India

    S. Veni & R. Anand

  2. Sona Signal and Image Processing Research Center, Sona College of Technology, Salem, India

    R. Anand

  3. Robert Bosch, Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidhayapeetham, Amrita University, Coimbatore, 641112, India

    B. Santosh

Authors
  1. S. Veni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. Santosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Anand .

Editor information

Editors and Affiliations

  1. Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Asis Kumar Tripathy

  2. San Diego State University, San Diego, CA, USA

    Mahasweta Sarkar

  3. Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, India

    Jyoti Prakash Sahoo

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Suchismita Chinara

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Veni, S., Anand, R., Santosh, B. (2021). Road Accident Detection and Severity Determination from CCTV Surveillance. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_25

Download citation

Publish with us

Policies and ethics

Search

Navigation