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Realtime Accident Detection and Alarm Generation System Over IoT

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Multimedia Technologies in the Internet of Things Environment, Volume 2

Part of the book series: Studies in Big Data ((SBD,volume 93))

Abstract

In the recent scenario, there is a drastic improvement in transportation, infrastructure, and communication technology which increases the number of commercial as well as non-commercial vehicles. Therefore, there is also an increase in the number of accident incidence. This ultimately results in a high death rate due to a road accident. More than half of accident incidence results in death due to delayed medical aid to the victim. If medical aid or services received at the proper time, then the victim may survive. With the application of machine learning processes and communication advancements, there is scope for the development of a more accurate system. In this chapter, a model is presented based on IoT devices that can sense and predict the pre-accident/pre-collision state and generates an alarm message about the collision is going to occur. This model is designed to extracts image/video features to determine the possibility of occurrence of a collision. This model is also efficient for post-collision.

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References

  1. Bhatti, F., Shah, M.A., Maple, C., Islam, S.U.: A novel internet of things-enabled accident detection and reporting system for smart city environments. Sensors 19(9), 2071 (2019)

    Article  Google Scholar 

  2. Nanda, S., Joshi, H., Khairnar, S.: An IOT based smart system for accident prevention and detection. In: Proceedings—2018 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018 (2018). https://doi.org/10.1109/ICCUBEA.2018.8697663

  3. Venkat, T., Rao, N., Reddy, K., Student, Y.: Preventing drunken driving accidents using IoT. Int. J. Adv. Res. Comput. Sci. 8(3):397–400 (2017). www.ijarcs.info

  4. Edwards, J.B.: The relationship between road accident severity and recorded weather. J. Safety Res. 29(4), 249–262 (1998). https://doi.org/10.1016/S0022-4375(98)00051-6

    Article  Google Scholar 

  5. Yue, M., Fan, L., Shahabi, C.: Traffic accident detection with spatiotemporal impact measurement. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10938 LNAI, 471–482 (2018). https://doi.org/10.1007/978-3-319-93037-4_37

  6. Syedul Amin, M., Jalil, J., Reaz, M.B.I.: Accident detection and reporting system using GPS, GPRS and GSM technology. In: 2012 International Conference on Informatics, Electronics and Vision, ICIEV 2012, 640–643 (2012)

    Google Scholar 

  7. Alvi, U., Khattak, M.A.K., Shabir, B., Malik, A.W., Muhammad, S.R.: A comprehensive study on IoT based accident detection systems for smart vehicles. IEEE Access 8, 122480–122497 (2020)

    Article  Google Scholar 

  8. Singh, D., Mohan, C.K.: Deep spatio-temporal representation for detection of road accidents using stacked autoencoder. IEEE Trans. Intell. Transp. Syst. 20(3), 879–887 (2019). https://doi.org/10.1109/TITS.2018.2835308

    Article  MathSciNet  Google Scholar 

  9. Ki, Y.K., Lee, D.Y.: A traffic accident recording and reporting model at intersections. IEEE Trans. Intell. Transp. Syst. 8(2), 188–194 (2007). https://doi.org/10.1109/TITS.2006.890070

    Article  Google Scholar 

  10. Hui, Z., Xie, Y., Lu, M., Fu, J.:Vision-based real-time traffic accident detection. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2015-(March), 1035–1038 (2015). https://doi.org/10.1109/WCICA.2014.7052859

  11. Aköz, Ö., Elif-Karsligil, M.: Video-based traffic accident analysis at intersections using partial vehicle trajectories. In: Proceedings—International Conference on Image Processing, ICIP 4693–4696 (2010). https://doi.org/10.1109/ICIP.2010.5653839

  12. Sadek, S., Al-Hamadi, A., Michaelis, B., Sayed, U.: Real-time automatic traffic accident recognition using HFG. In: Proceedings—International Conference on Pattern Recognition 3348–3351 (2010). https://doi.org/10.1109/ICPR.2010.817

  13. Kwon, J., Lee, K.M.: Wang-landau monte carlo-based tracking methods for abrupt motions. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 1011–1024 (2013). https://doi.org/10.1109/TPAMI.2012.161

    Article  MathSciNet  Google Scholar 

  14. Lim, M.K., Chan, C.S., Monekosso, D., Remagnino, P.: Refined particle swarm intelligence method for abrupt motion tracking. Inf. Sci. 283, 267–287 (2014). https://doi.org/10.1016/j.ins.2014.01.003

    Article  Google Scholar 

  15. Su, Y., Zhao, Q., Zhao, L., Gu, D.: Abrupt motion tracking using a visual saliency embedded particle filter. Pattern Recogn. 47(5), 1826–1834 (2014). https://doi.org/10.1016/j.patcog.2013.11.028

    Article  Google Scholar 

  16. Biral, F., Lot, R., Rota, S., Fontana, M., Huth, V.: Intersection support system for powered two-wheeled vehicles: threat assessment based on a receding horizon approach. IEEE Trans. Intell. Transp. Syst. 13(2), 805–816 (2012). https://doi.org/10.1109/TITS.2011.2181835

    Article  Google Scholar 

  17. Attal, F., Boubezoul, A., Oukhellou, L., Espie, S.: Riding patterns recognition for powered two-wheelers users’ behaviors analysis. In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC 2033–2038 (2013). https://doi.org/10.1109/ITSC.2013.6728528

  18. Attal, F., Boubezoul, A., Oukhellou, L., Cheifetz, N., Espie, S.: The powered two wheelers fall detection using multivariate clative SUM (MCUSUM) control charts. In: 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, 1280–1285 (2014). https://doi.org/10.1109/ITSC.2014.6957863

  19. Meena, A., Iyer, S., Nimje, M., Joglekar, S., Jagtap, S., Rahman, M.: Automatic accident detection and reporting framework for two wheelers. In: Proceedings of 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014, 962–967 (2015). https://doi.org/10.1109/ICACCCT.2014.7019237

  20. Harnett, C.K.: Open wireless sensor network telemetry platform for mobile phones. IEEE Sens. J. 10(6), 1083–1084 (2010). https://doi.org/10.1109/JSEN.2010.2040271

    Article  Google Scholar 

  21. Barmpounakis, E.N., Vlahogianni, E.I., Golias, J.C.: Intelligent transportation systems and powered two wheelers traffic. IEEE Trans. Intell. Transp. Syst. 17(4), 908–916 (2016). https://doi.org/10.1109/TITS.2015.2497406

    Article  Google Scholar 

  22. Rajarapollu, P.R., Bansode, N.V., Mane, P.P.: A novel two wheeler security system based on embedded system. In: Proceedings—2016 International Conference on Advances in Computing, Communication and Automation (Fall), ICACCA 2016 (2016). https://doi.org/10.1109/ICACCAF.2016.7748974

  23. Basheer, F.B., Alias, J.J., Favas, C.M., Navas, V., Farhan, N.K., Raghu, C.V.: Design of accident detection and alert system for motor cycles. C2013 IEEE global humanitarian technology conference: South Asia satellite. GHTC-SAS 2013, 85–89 (2013). https://doi.org/10.1109/GHTC-SAS.2013.6629894

    Article  Google Scholar 

  24. Manjunatha, D., Ishwar, M., Ganesh, L.B.: Safety and security for two wheeler vehicle using ARM controller & CAN protocol. Int. Res. J. Eng. Technol. (IRJET) 3(6):1082–1084 (2016)

    Google Scholar 

  25. Abtahi, S., Hariri, B., Shirmohammadi, S.: Driver drowsiness monitoring based on yawning detection. In: Conference Record—IEEE Instrumentation and Measurement Technology Conference, 1606–1610 (2011). https://doi.org/10.1109/IMTC.2011.5944101

  26. Sivakumara, Dhivyap.: Smart helmet system using alcohol detection for vehicle protection. Int. J. Adv. Sci. Eng. Res (2016). www.ijaser.in

  27. Chandran, S., Chandrasekar, S., Elizabeth, N.E.: Konnect: an Internet of Things (IoT) based smart helmet for accident detection and notification. In: 2016 IEEE Annual India Conference, INDICON 2016 (2017). https://doi.org/10.1109/INDICON.2016.7839052

  28. Vatti, N.R., Vatti, P., Vatti, R., Garde, C.: Smart road accident detection and communication system. In: Proceedings of the 2018 International Conference on Current Trends Towards Converging Technologies, ICCTCT 2018 (2018). https://doi.org/10.1109/ICCTCT.2018.8551179

  29. Dar, B.K., Shah, M.A., Islam, S.U., Maple, C., Mussadiq, S., Khan, S.: Delay-aware accident detection and response system using fog computing. IEEE Access 7, 70975–70985 (2019). https://doi.org/10.1109/ACCESS.2019.2910862

    Article  Google Scholar 

  30. Yu, Y., Xu, M., Gu, J.: Vision-based traffic accident detection using sparse spatio-temporal features and weighted extreme learning machine. IET Intel. Transp. Syst. 13(9), 1417–1428 (2019). https://doi.org/10.1049/iet-its.2018.5409

    Article  Google Scholar 

  31. Rajesh, G., Benny, A.R., Harikrishnan, A., Jacobabraham, J., John, N.P.: A deep learning based accident detection system. In: Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020, 1322–1325 (2020). https://doi.org/10.1109/ICCSP48568.2020.9182224

  32. Ghosh, S., Sunny, S.J., Roney, R.: Accident detection using convolutional neural networks. In: 2019 International Conference on Data Science and Communication, IconDSC 2019 (2019). https://doi.org/10.1109/IconDSC.2019.8816881

  33. Lee, K.B., Shin, H.S.: An application of a deep learning algorithm for automatic detection of unexpected accidents under bad CCTV monitoring conditions in tunnels. In: Proceedings—2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019, 7–11 (2019). https://doi.org/10.1109/Deep-ML.2019.00010

  34. Ren, H., Song, Y., Wang, J., Hu, Y., Lei, J.: A deep learning approach to the citywide traffic accident risk prediction. In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018-November, 3346–3351 (2018). https://doi.org/10.1109/ITSC.2018.8569437

  35. Zheng, M., Li, T., Zhu, R., Chen, J., Ma, Z., Tang, M., Cui, Z., Wang, Z.: Traffic accident’s severity prediction: a deep-learning approach-based CNN network. IEEE Access 7, 39897–39910 (2019). https://doi.org/10.1109/ACCESS.2019.2903319

    Article  Google Scholar 

  36. Zhang, Z., He, Q., Gao, J., Ni, M.: A deep learning approach for detecting traffic accidents from social media data. Transp. Res. Part C Emerging Technol. 86, 580–596 (2018). https://doi.org/10.1016/j.trc.2017.11.027

    Article  Google Scholar 

  37. Dabiri, S., Heaslip, K.: Developing a Twitter-based traffic event detection model using deep learning architectures. Expert Syst. Appl. 118, 425–439 (2019). https://doi.org/10.1016/j.eswa.2018.10.017

    Article  Google Scholar 

  38. D’Andrea, E., Ducange, P., Lazzerini, B., Marcelloni, F.: Real-time detection of traffic from twitter stream analysis. IEEE Trans. Intell. Transp. Syst. 16(4), 2269–2283 (2015). https://doi.org/10.1109/TITS.2015.2404431

    Article  Google Scholar 

  39. Chen, C., Fan, X., Zheng, C., Xiao, L., Cheng, M., Wang, C.: SDCAE: stack denoising convolutional autoencoder model for accident risk prediction via traffic big data. In: Proceedings—2018 6th International Conference on Advanced Cloud and Big Data, CBD 2018, 328–333 (2018). https://doi.org/10.1109/CBD.2018.00065

  40. https://www.infinitiofcoconutcreek.com/blogs/732/how-does-infinity-predict-accidents/

  41. https://www.ford.com/technology/driver-assist-technology/pre-collision-assist/#:~:text=The%20Pre%2DCollision%20Assist%20feature,can%20eliminate%2C%20a%20frontal%20collision.

  42. https://global.honda/innovation/technology/automobile/Honda-Sensing.html

  43. https://www.la.mercedes-benz.com/en/passengercars/mercedes-benz-cars/models/slc/slc-roadster/explore.pi.html/mercedes-benz-cars/models/slc/slc-roadster/explore/intelligent-technologies/active-brake-assistance

  44. http://www.toyota.com.cn/innovation/safety_technology/safety_technology/pre-crash_safety/

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Goyal, S.B., Bedi, P., Kumar, J., Ankita (2022). Realtime Accident Detection and Alarm Generation System Over IoT. In: Kumar, R., Sharma, R., Pattnaik, P.K. (eds) Multimedia Technologies in the Internet of Things Environment, Volume 2. Studies in Big Data, vol 93. Springer, Singapore. https://doi.org/10.1007/978-981-16-3828-2_6

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  • DOI: https://doi.org/10.1007/978-981-16-3828-2_6

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