Yassin Kortli; Mehrez Marzougui; Mohamed Atri

High-reliability Vehicle Detection and Lane Collision Warning System

Full Text (PDF, 823KB), PP.1-14

Views: 0 Downloads: 0

Author(s)

Yassin Kortli ^1,* Mehrez Marzougui ² Mohamed Atri ¹

1. Electronic and Micro-electronic Laboratory, Faculty of Sciences of Monastir, University of Monastir

2. Department of Computer Engineering, King Khalid University

* Corresponding author.

DOI: https://doi.org/10.5815/ijwmt.2018.02.01

Received: 19 Jan. 2018 / Revised: 27 Jan. 2018 / Accepted: 13 Feb. 2018 / Published: 8 Mar. 2018

Index Terms

Driving Assistance Systems (DAS), Front Collision Warning System (FCWS), Otsu threshold, Histogram of Oriented Gradient (HOG), Support Vector Machine (SVM).

Abstract

In the last two decades, developing Driving Assistance Systems for security has been one of the most active research fields in order to minimize traffic accidents. Vehicle detection is a vital operation in most of these applications. In this paper, we present a high reliable and real-time lighting-invariant lane collision warning system. We implement a novel real-time vehicles detection using Histogram of Oriented Gradient and Support Vector Machine which could be used for collision prediction. Thus, in order to meet the conditions of real-time systems and to reduce the searching region, Otsu’s threshold method play a critical role to extract the Region of Interest using the gradient information firstly. Secondly, we use Histogram of Oriented Gradient (HOG) descriptor to get the features vector, and these features are classified using a Support Vector Machine (SVM) classifier to get training base. Finally, we use this base to detect the vehicles in the road. Two sets generated the training data of our system a set of negative images (non-vehicles) a set of positive images (vehicles), and the test is performed on video sequences on the road. The proposed methodology is tested in different conditions. Our experimental results and accuracy evaluation indicates the efficiency of your system proposed for vehicles detection.

Cite This Paper

Yassin Kortli, Mehrez Marzougui, Mohamed Atri," High-reliability Vehicle Detection and Lane Collision Warning System", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.8, No.2, pp. 1-14, 2018. DOI: 10.5815/ijwmt.2018.02.01

Reference

[1]HUANG, Shih-Shinh, CHEN, Chung-Jen, HSIAO, Pei-Yung, et al. On-board vision system for lane recognition and frontvehicle detection to enhance driver's awareness. In : Robotics and Automation, 2004. Proceedings. ICRA'04. 2004 IEEE International Conference on. IEEE, 2004. p. 2456-2461.

[2]Z SUN, Zehang, BEBIS, George, et MILLER, Ronald. On-road vehicle detection using Gabor filters and support vector machines. In: Digital Signal Processing, 2002. DSP 2002. 2002 14th International Conference on. IEEE, 2002. p. 1019-1022.

[3]KARADUMAN, O., EREN, H., KURUM, H., et al. Approaching car detection via clustering of vertical-horizontal line scanning optical edge flow. In: Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on. IEEE, 2012. p. 502-507.

[4]XIONG, Bin et DING, Xiaoqing. A generic object detection using a single query image without training. Tsinghua Science and Technology, 2012, vol. 17, no 2, p. 194-201.

[5]XU, Yanwu, XU, Dong, LIN, Stephen, et al. Detection of sudden pedestrian crossings for driving assistance systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, vol. 42, no 3, p. 729-739.

[6]BARCELLOS, Pablo, GOMES, Vitor, et SCHARCANSKI, Jacob. Shadow detection in camera-based vehicle detection: survey and analysis. Journal of Electronic Imaging, 2016, vol. 25, no 5, p. 051205-051205.

[7]MOHAMED, Atibi, ISSAM, Atouf, MOHAMED, Boussaa, et al. Real-time Detection of Vehicles Using the Haar-like Features and Artificial Neuron Networks. Procedia Computer Science, 2015, vol. 73, p. 24-31.

[8]DALAL, Navneet et TRIGGS, Bill. Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. IEEE, 2005. p. 886-893.

[9]CAI, Yingfeng, SUN, Xiaoqiang, WANG, Hai, et al. Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning. Journal of Sensors, 2016, vol. 2016.

[10]YONG, Xi, ZHANG, Liwei, SONG, Zhangjun, et al. Real-time vehicle detection based on Haar features and Pairwise Geometrical Histograms. In: Information and Automation (ICIA), 2011 IEEE International Conference on. IEEE, 2011. p. 390-395.

[11]O. Nobuyuki, "A threshold selection method from gray-level histograms." IEEE Transactions on Systems, Man, and Cybernetics, Vol.9, No. 1, pp. 62-66, 1979.

[12]Nagashree, R. N., and N. Aswini. "Approaches of Buried Object Detection Technology." International Journal of Wireless and Microwave Technologies (IJWMT), (2014).

[13]Aetesam, Hazique, and Itu Snigdh. "A Survey on Topology Maintenance in Wireless Sensor Networks." International Journal of Wireless and Microwave Technologies (IJWMT) 6.4 (2016): 29-37.

[14]Pradhan, Shibashis, Sudipta Chattopadhyay, and Sujatarani Raut. "A novel orthogonal minimum correlation spreading code in CDMA system." IJ Wirless and Microwave Technologies 2 (2014): 38-52.

International Journal of Wireless and Microwave Technologies (IJWMT)