Abstract
Self-driving cars have become inevitable to be present in a near future. A big number of large companies, startups and research groups have been working for years to fulfil the vision of an absolute unmanned transportation system. These systems have the capacity to model how our future societies and livelihood will be shaped. The utopian dream may still be years away, but current researchers have been shaping up for this tomorrow little by little. From a rise and advances in the field of deep learning, there has been a major push received, especially in the field of computer vision and its possibly uncountable applications. Autonomous vehicles have grown a lot over the past decade from development of better computer vision algorithms for solving common driving tasks, to coming up with large datasets which support training of such systems. In this paper, we have aimed to give a brief introduction to research trends being followed in the overlapping areas of self-driving cars and computer vision. Some state of the art algorithms for solving some common problems which an autonomous system can be benefitted from, such as object detection and semantic scene segmentation, are also discussed.
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
Similar content being viewed by others
References
Road accidents in India decrease by 4.1% during 2016, fatalities rise by 3.2%. http://pib.nic.in/newsite/PrintRelease.aspx?relid=170577, last accessed 27 Mar. 2018
Manyika, J., Chui, M.: Disruptive Technologies: Advances that Will Transform Life, Business, and the Global Economy. McKinsey Global Institute (2013)
Bullis, K.: How Vehicle Automation Will Cut Fuel Consumption. MIT Technology Review (2011)
Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Poggio, T.: Pedestrian detection using wavelet templates. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 17 Jun 1997, pp. 193–199
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2005, IEEE, 25 Jun 2005, vol. 1, pp. 886–893
Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, IEEE, 1999, vol. 2
Jensen, M.B., Philipsen, M.P., Mogelmose, A., Moeslund, T.B., Trivedi, M.M.: Vision for looking at traffic lights: issues, survey, and perspectives. In: IEEE Transactions on Intelligent Transportation Systems (2015)
Agarwal, N., Sharma, A., Chang, J.R.: Real-time traffic light signal recognition system for a self-driving car. In: International Symposium on Signal Processing and Intelligent Recognition Systems 13 Sep 2017, pp. 276–284. Springer, Cham
Shi, Z., Zou, Z., Zhang, C.: Real-time traffic light detection with adaptive background suppression filter. IEEE Trans. Intell. Transp. Syst. 17(3), 690–700 (2016)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated Recognition, Localization and Detection Using Convolutional Networks. arXiv preprint
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 2014, pp. 580–587
Girshick, R.: Fast r-cnn (30 2015 Apr). arXiv preprint arXiv:1504.08083
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)
Pop, D.O., Rogozan, A., Nashashibi, F., Bensrhair, A.: Pedestrian recognition through different cross-modality deep learning methods. In: IEEE International Conference on Vehicular Electronics and Safety, Jun 2017, Vienna, Austria
Ray, S., Turi, R.H.: Determination of number of clusters in k-means clustering and application in colour image segmentation. In: Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques, pp. 137–143, 27 Dec 1999
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)
He, X., Zemel, R.S., Carreira-Perpiñán, M.Á.: Multiscale conditional random fields for image labeling. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 27 Jun 2004. CVPR 2004, vol. 2, pp. II–II
Badino, H., Franke, U., Pfeiffer, D.: The stixel world-a compact medium level representation of the 3d-world. In: Joint Pattern Recognition Symposium 2009. Springer, Berlin, Heidelberg
Pfeiffer, D., Franke, U.: Towards a global optimal multi-layer Stixel representation of dense 3D data. In: BMVC Aug 2011, vol. 11, pp. 51–51
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1537 (2015)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 22 Oct 2017, pp. 2980–2988
Janai, J., Güney, F., Behl, A., Geiger, A.: Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017). arXiv preprint arXiv:1704.05519
Hosang, J., Omran, M., Benenson, R., Schiele, B.: Taking a deeper look at pedestrians. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Abdic, I., Fridman, L., McDuff, D., Marchi, E., Reimer, B., Schuller, B.: Driver frustration detection from audio and video in the wild. In: Proceedings of the KI 2016: Advances in Artificial Intelligence: 39th Annual German Conference on AI, Klagenfurt, Austria, 26–30 Sept 2016, 8 Sep 2016, vol. 9904, p. 237. Springer
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Agarwal, N., Chiang, CW., Sharma, A. (2019). A Study on Computer Vision Techniques for Self-driving Cars. In: Hung, J., Yen, N., Hui, L. (eds) Frontier Computing. FC 2018. Lecture Notes in Electrical Engineering, vol 542. Springer, Singapore. https://doi.org/10.1007/978-981-13-3648-5_76
Download citation
DOI: https://doi.org/10.1007/978-981-13-3648-5_76
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3647-8
Online ISBN: 978-981-13-3648-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)