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Road Accident Detection and Severity Determination from CCTV Surveillance

  • S. Veni
  • R. AnandEmail author
  • B. Santosh
Conference paper
  • 21 Downloads
Part of the Lecture Notes in Networks and Systems book series (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.

Keywords

Motion field Optical flow Farneback optical flow Angle vector 

References

  1. 1.
    World Health Organization (2015) Global status report on road safety 2015. World Health Organization
  2. 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
  3. 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
  4. 4.
    Manocha M, Kaur P (2014) Object tracking techniques for video tracking: a survey. Int J Eng Sci (IJES) 3(6)
  5. 5.
    Rout RK (2013) A survey on object detection and tracking algorithms. Ph.D. dissertation
  6. 6.
    Gunnar F (2002) Polynomial expansion for orientation and motion estimation. , Ph. D. dissertation. Linköping University Electronic Press
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 13.
    Ki Y-K (2007) Accident detection system using image processing and MDR. Int J Comput Sci Network Secur (IJCSNS) 7(3):35–39
  14. 14.
    McCahill M, Norris C (2002) CCTV in London. Report deliverable of UrbanEye project
  15. 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
  16. 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
  17. 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

Copyright information

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

Authors and Affiliations

  1. 1.Amrita School of Engineering, Amrita Vishwa Vidhayapeetham, Amrita UniversityCoimbatoreIndia
  2. 2.Sona Signal and Image Processing Research CenterSona College of TechnologySalemIndia
  3. 3.Robert Bosch, Department of Mechanical EngineeringAmrita School of Engineering, Amrita Vishwa Vidhayapeetham, Amrita UniversityCoimbatoreIndia

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