Abstract
The European eSafety initiative aims to improve the safety and efficiency of road transport. The main element of eSafety is the pan European eCall project – an in-vehicle system that informs about road collisions or serious accidents. An on-board compact eCall device which can be installed in used vehicle is being developed, partially with the authors of the paper. The proposed system is independent of built-in car systems, it is able to detect a road accident, indicate the number of occupants inside the vehicle, report their vital functions and send those information to dedicated emergency services via duplex communication channel.
This paper focuses on an important functionality of such a device: vehicle occupants detection and counting. The authors analyze a wide variety of sensors and algorithms that can be used and present results of their experiments based on video feed.
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References
Road Safety: Encouraging results in 2016 call for continued efforts to save lives on EU roads. http://europa.eu/rapid/press-release_IP-17-674_en.htm. Accessed 24 Mar 2018
eCall: Time saved = lives saved. https://ec.europa.eu/digital-single-market/en/eCall-time-saved-lives-saved. Accessed 24 Mar 2018
European Parliament makes eCall mandatory from 2018. http://www.etsi.org/news-events/news/960-2015-05-european-parliament-makes-ecall-mandatory-from-2018. Accessed 24 Mar 2018
System sensorowy w pojazdach do rozpoznania stanu po wypadku z transmisja informacji do punktu przyjmowania zgloszen eCall. http://pb.edu.pl/projekty-pb/ecall. Accessed 24 Mar 2018
Nguyen, D.T., Li, W., Ogunbona, P.O.: Human detection from images and videos: a survey. Pattern Recognit. 51, 148–175 (2016)
Choi, J.W., Yim, D.H., Cho, S.H.: People counting based on an IR-UWB radar sensor. IEEE Sens. J. 17, 5717–5727 (2017)
Zohn, Bc.L.: Detection of persons in a vehicle using IR cameras. Master’s Thesis, Faculty of Transportation Sciences, Czech Technical University in Prague (2016)
Bernini, N., Bombini, L., Buzzoni, M., Cerri, P., Grisleri, P.: An embedded system for counting passengers in public transportation vehicles. In: 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications Proceedings (2014)
Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)
Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Stat. 26(5), 1651–1686 (1998)
Zhihua, Z.: Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC, Boca Raton (2012)
Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: ICPR (2004)
Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognit. Lett. 27, 773 (2006)
Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intel. 17(8), 790–799 (1995)
Bradski, G.: Computer vision face tracking for use in a perceptual user interface. Intel Technol. J. 2(2), 1–15 (1998)
Exner, D., Bruns, E., Kurz, D., Grundhöfer, A., Bimber, O.: Fast and robust CAMShift tracking. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, USA, pp. 9–16 (2010)
Sooksatra, S., Kondo, T.: CAMShift-based algorithm for multiple object tracking. In: Proceedings of the 9th International Conference on Computing and Information Technology IC2IT 2013, Bangkok, Thailand, pp. 301–310 (2013)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of Imaging Understanding Workshop (1981)
Abdi, H., William, L.J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433–459 (2010)
Hyvarinen, A., Karhunen, J., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13, 411–430 (2000)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, vol. 13, pp. 556–562 (2001)
Rabiner, L.R.: A tutorial o hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989)
Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Signal Process. 7(3–4), 197–387 (2014)
Ramírez, J., Górriz, J.M., Segura, J.C.: Voice activity detection, fundamentals and speech recognition system robustness. In: Robust Speech Recognition and Understanding, pp. 1–22 (2007)
Lupinska-Dubicka, A., Tabedzki, M., Adamski, M., Rybnik, M., Omieljanowicz, M., Omieljanowicz, A., Szymkowski, M., Gruszewski, M., Klimowicz, A., Rubin, G., Saeed, K.: The concept of in-vehicle system for human presence and their vital signs detection. In: 5th International Doctoral Symposium on Applied Computation and Security Systems: ACSS2018 (2018)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Vadsohn. http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/doc/voicebox/vadsohn.html. Accessed 30 Apr 2018
Fastica. https://www.cs.helsinki.fi/u/ahyvarin/papers/fastica.shtml. Accessed 30 Apr 2018
Abercrombie, L.: Short poetry collection 091. http://librivox.org. Accessed 30 Apr 2018
Soundjay. https://www.soundjay.com/. Accessed 30 Apr 2018
Soundbible. http://soundbible.com/tags-driving.html. Accessed 30 Apr 2018
https://www.best-selling-cars.com/europe/2016-full-year-europe-best-selling-car-manufacturers-brands/. Accessed 24 Mar 2018
Eurostat - Passenger cars in the EU. http://ec.europa.eu/eurostat/statistics-explained/index.php/Passenger_cars_in_the_EU. Accessed 24 Mar 2018
Bellucci, P., Cipriani, E.: Data accuracy on automatic traffic counting the smart project results. Eur. Transp. Res. Rev. 2(4), 175–187 (2010)
Acknowledgments
The authors would like to sincerely thank Professor Khalid Saeed for content-related care, inspiration and motivation to work.
This work was supported by grant S/WI/1/2018 and S/WI/2/2018, and S/WI/3/2018 from Bialystok University of Technology and funded with resources for research by the Ministry of Science and Higher Education in Poland.
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Lupinska-Dubicka, A. et al. (2019). Vehicle Passengers Detection for Onboard eCall-Compliant Devices. In: Pejaś, J., El Fray, I., Hyla, T., Kacprzyk, J. (eds) Advances in Soft and Hard Computing. ACS 2018. Advances in Intelligent Systems and Computing, vol 889. Springer, Cham. https://doi.org/10.1007/978-3-030-03314-9_35
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