Abstract
Vulnerable road user safety is of paramount importance as transport moves towards fully autonomous driving. The research question posed by this research is of how can we train a computer to be able to see and perceive a pedestrian’s movement. This work presents a dual network architecture, trained in tandem, which is capable of classifying the behaviour of a pedestrian from a single image with no prior context. The results show that the most successful network was able to achieve a correct classification accuracy of 94.3% when classifying images based on their behaviour. This shows the use of a novel data fusion method for pedestrian images and human poses. Having a network with these capabilities is important for the future of transport, as it will allow vehicles to correctly perceive the intention of pedestrians crossing the street, and will ultimately lead to fewer pedestrian casualties on our roads.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields, pp. 1–14, December 2018. http://arxiv.org/abs/1812.08008
Dalal, N., Triggs, W.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, no. 3, pp. 886–893 (2004)
[DATASET], Karlsruhe Institute of Technology, and Toyota Technological Institute at Chigaco. KITTI Vision Benchmark Suite (2011)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Li, F.-F.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)
Dollar, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: Procedings of the British Machine Vision Conference 2010, pp. 68.1–68.11 (2010)
Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, pp. 304–311 (2009)
Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)
Du, X., El-Khamy, M., Morariu, V.I., Lee, J., Davis, L.: Fused deep neural networks for efficient pedestrian detection, pp. 1–11, May 2018
Fang, H., Xie, S., Tai, Y., Lu, C.: RMPE: regional multi-person pose estimation. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2353–2362. IEEE, October 2017
Fang, Z., López, A.M.: Is the pedestrian going to cross? Answering by 2D pose estimation. In: Proceedings of IEEE Intelligent Vehicles Symposium, June–July 2018, pp. 1271–1276 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR 2016 (2016)
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks, August 2016. http://arxiv.org/abs/1608.06993
Kohli, P., Chadha, A.: Enabling pedestrian safety using computer vision techniques: a case study of the 2018 Uber Inc. self-driving car crash (2018). http://arxiv.org/abs/1805.11815
Kotseruba, I., Rasouli, A., Tsotsos, J.K.: Joint Attention in Autonomous Driving (JAAD). 1–10 (2016). http://arxiv.org/abs/1609.04741
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances In Neural Information Processing Systems, pp. 1–9 (2012)
Reynolds, S., Tranter, M., Baden, P., Mais, D., Dhani, A., Wolch, E., Bhagat, A.: Reported road casualties Great Britain: 2016. Technical report, September 2017
Sermanet, P., Kavukcuoglu, K., Chintala, S., Lecun, Y.: Pedestrian detection with unsupervised multi-stage feature learning. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3626–3633 (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. 1–14 (2014). http://arxiv.org/abs/1409.1556
Sucha, M., Dostal, D., Risser, R.: Pedestrian-driver communication and decision strategies at marked crossings. Accid. Anal. Prev. 102, 41–50 (2017)
Thompson, L.L., Rivara, F.P., Ayyagari, R.C., Ebel, B.E.: Impact of social and technological distraction on pedestrian crossing behaviour: an observational study. Inj. Prev. 19(4), 232–237 (2013)
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, CVPR 2001, vol. 1, pp. I–511–I–518 (2004)
Yan, J., Zhang, X., Lei, Z., Liao, S., Li, S.Z.: Robust multi-resolution pedestrian detection in traffic scenes. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3033–3040 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Spooner, J., Cheah, M., Palade, V., Kanarachos, S., Daneshkhah, A. (2020). Classification of a Pedestrian’s Behaviour Using Dual Deep Neural Networks. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1230. Springer, Cham. https://doi.org/10.1007/978-3-030-52243-8_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-52243-8_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-52242-1
Online ISBN: 978-3-030-52243-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)