Abstract
Electric scooters (e-scooters) have been considered as a “last mile” solution to existing public transportation systems in many cities all over the world due to their convenience at a highly affordable price. E-scooters enable users to travel distances which are too long to walk but too short to drive, so they help to reduce the number of cars on the roads. Along with its increasing popularity, accidents involving e-scooters have become a growing public concern, especially in large cities with heavy traffic.
It is useful to detect and include e-scooters in traffic control. However, there is no available pre-built-model for detecting an electric scooter. Therefore, in this paper, we proposed a scooter and its rider detection framework that supports emergency management for scooter-related injuries. The framework helps to identify scooter and its rider in live-stream videos and can be applied in traffic incidents detection applications. Our model was developed based on deep learning object detection models. Using ImageAI API, we trained and deployed our own model based on 200 images acquired on the internet. The preliminary results appeared to be robust and fast; however, the accuracy of our proposed model could be improved if using a larger dataset for training and evaluating.
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References
Beck, S., Barker, L., Chan, A., Stanbridge, S.: Emergency department impact following the introduction of an electric scooter sharing service. Emerg. Med. Australas. 32(3), 409–415 (2020)
Sipe, N., Pojani, D.: Can e-scooters solve the ‘last mile’ problem? They’ll need to avoid the fate of dockless bikes (2018)
Aizpuru, M., Farley, K.X., Rojas, J.C., Crawford, R.S., Moore Jr., T.J., Wagner, E.R.: Motorized scooter injuries in the era of scooter-shares: a review of the national electronic surveillance system. Am. J. Emerg. Med. 37(6), 1133–1138 (2019)
DiMaggio, C.J., Bukur, M., Wall, S.P., Frangos, S.G., Wen, A.Y.: Injuries associated with electric-powered bikes and scooters: analysis of US consumer product data. Injury Prev. 26(6), 524–528 (2019)
Che, M., Lum, K.M., Wong, Y.D.: Users’ attitudes on electric scooter riding speed on shared footpath: a virtual reality study. Int. J. Sustain. Transp. 15, 1–10 (2020)
Trivedi, T.K., et al.: Injuries associated with standing electric scooter use. JAMA Netw. Open 2(1), e187381–e187381 (2019)
Mitchell, G., Tsao, H., Randell, T., Marks, J., Mackay, P.: Impact of electric scooters to a tertiary emergency department: 8-week review after implementation of a scooter share scheme. Emerg. Med. Australas. 31(6), 930–934 (2019)
Mayhew, L.J., Bergin, C.: Impact of e-scooter injuries on emergency department imaging. J. Med. Imaging Radiat. Oncol. 63(4), 461–466 (2019)
Terrell, G.C.: Characterizing electric scooter riding behavior to detect unsafe and non-compliant use (2019)
Tang, C., Feng, Y., Yang, X., Zheng, C., Zhou, Y.: The object detection based on deep learning. In: 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 723–728. IEEE, China (2017)
Chen, Z., Khemmar, R., Decoux, B., Atahouet, A., Ertaud, J.Y.: Real time object detection, tracking, and distance and motion estimation based on deep learning: application to smart mobility. In: Eighth International Conference on Emerging Security Technologies (EST), pp. 1–6. IEEE, UK (2019)
Kozakaya, T., Shibata, T., Yuasa, M., Yamaguchi, O.: Facial feature localization using weighted vector concentration approach. Image Vision Comput. 28(5), 772–780 (2010)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE, USA (2005)
Zhang, G., Huang, X., Li, S.Z., Wang, Y., Wu, X.: Boosting local binary pattern (LBP)-based face recognition. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 179–186. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30548-4_21
Leistner, C., Roth, P.M., Grabner, H., Bischof, H., Starzacher, A., Rinner, B.: Visual on-line learning in distributed camera networks. In: 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras, pp. 1–10. IEEE, USA (2008)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Dhande, M.: What is the difference between AI, machine learning and deep learning. Geospatial World (2017)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE, USA (2014)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448. IEEE, Chile (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788. IEEE, USA (2016)
Wang, H., Zhang, Z.: Text detection algorithm based on improved YOLOv3. In: 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), pp. 147–150. IEEE, China (2019)
Wu, F., Jin, G., Gao, M., Zhiwei, H.E., Yang, Y.: Helmet detection based on improved yolo v3 deep model. In: 16th International Conference on Networking. Sensing and Control (ICNSC), pp. 363–368. IEEE, Canada (2019)
Yajai, A., Rodtook, A., Chinnasarn, K., Rasmequan, S.: Fall detection using directional bounding box. In: 12th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 52–57. IEEE, USA (2015)
Nowozin, S.: Optimal decisions from probabilistic models: the intersection-over-union case. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 548–555. IEEE, USA (2014)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)
Hosang, J., Benenson, R., Schiele, B.: Learning non-maximum suppression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4507–4515. IEEE, USA (2017)
Yajai, A., Rodtook, A., Chinnasarn, K., Rasmequan, S.: Fall detection using directional bounding box. In: 2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 52–57. IEEE, July 2015
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Nguyen, H., Nguyen, M., Sun, Q. (2021). Electric Scooter and Its Rider Detection Framework Based on Deep Learning for Supporting Scooter-Related Injury Emergency Services. In: Nguyen, M., Yan, W.Q., Ho, H. (eds) Geometry and Vision. ISGV 2021. Communications in Computer and Information Science, vol 1386. Springer, Cham. https://doi.org/10.1007/978-3-030-72073-5_18
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DOI: https://doi.org/10.1007/978-3-030-72073-5_18
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