Abstract
This paper details about a framework for preceding obstacle detection for autonomous vehicles or driver assistance systems (DAS). Detection and tracking surrounding moving obstacles such as vehicles and pedestrians are crucial for the safety of people and autonomous vehicles. The proposed system uses four main stages: input, feature extraction, obstacle detection, and output of system. The system has input in video format which will be converted into frames. Feature extraction stage extracts LBP+LTP feature and Gabor feature separately. With the help of training set and extracted features, CNN will detect the obstacle in the frame. The system with LBP+LTP feature in cooperation with CNN gives 86.73% detection rate and 4% false alarm rate and the system with Gabor feature in cooperation with CNN gives 86.21% detection rate and 0.27% false alarm rate. From the proposed system, we can conclude that computer vision in combination with deep learning has the potential to bring about a relatively inexpensive, robust solution to autonomous driving.
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
Nguyen VD, Nguyen TT, Nguyen DD, Lee SJ, Jeon JW (2013) A fast evolutionary algorithm for real-time vehicle detection. IEEE Trans Veh Technol 62(6)
Keller CG, Enzweiler M, Rohrbach M, Llorca DF (2011) The benefits of dense stereo for pedestrian detection. IEEE Trans Intell Trans Syst 12(4)
Yu H, Hong R, Huang XL, Wang Z (2013) Obstacle detection with deep convolutional neural network. In: Sixth international symposium on computational intelligence and design
Huval B, Wang T, Tandon S, Kiske J, Song W, Pazhayampallil J (2015) An empirical evaluation of deep learning on highway driving
Erbs F, Barth A, Franke U (2011) Moving vehicle detection by optimal segmentation of the dynamic stixel World. In: 2011 IEEE Intelligent Vehicles Symposium (IV) Baden-Baden, Germany, 5–9 June 2011
Mao X, Inoue D, Kato S, Kagami M (2012) Amplitude-modulated laser radar for range and speed measurement in car applications. IEEE Trans Intell Transp Syst 13(1):408–413
Sato S, Hashimoto M, Takita M, Takagi K, Ogawa T (2010) Multilayer lidar-based pedestrian tracking in urban environments. In: Proceeding of IEEE IV, pp 849–854
Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20(3), 273–297
Wang T, Bu L, Huang Z (2015) A new method for obstacle detection based on Kinect depth image. IEEE
Nuevo J, Parra I, Sjoberg J, Bergasa L (2010) Estimating surrounding vehicles’ pose using computer vision. In: Proceeding of 13th ITSC, pp 1863–1868
Liu T, Zheng N, Zhao L, Cheng H (2005) Learning based symmetric features selection for vehicle detection. In: Proceeding of IEEE Intelligence Vehicle Symposium, pp 124–129
Nguyen VD, Nguyen HV, Tran DT, Lee S, Jeon J (2016) Learning framework for robust obstacle detection, recognition, and tracking. IEEE Trans Intell Transp Syst
Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification
Prasad VSN Gabor filter visualization
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
Kore, P., Khoje, S. (2019). Obstacle Detection for Auto-Driving Using Convolutional Neural Network. In: Kulkarni, A., Satapathy, S., Kang, T., Kashan, A. (eds) Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-1610-4_28
Download citation
DOI: https://doi.org/10.1007/978-981-13-1610-4_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1609-8
Online ISBN: 978-981-13-1610-4
eBook Packages: EngineeringEngineering (R0)