Advanced driver assistant system based on monocular camera

An estimated 20-50 million people get injured and at least 1.2 million people die in automotive accidents every year. To avoid this, several security systems such as lane departure warning system, vehicle detection system, advanced cruise control system, etc., have been developed. But they are very expensive. In our research, we focus on developing low cost intelligent vehicle systems. In this paper, we propose a driver assistant system based on monocular camera. The proposed system consists of two parts. First part is vehicle detection module based on adaptive boosting using extent modified census transform (EMCT). Second part is lane detection module using RANdom Sample Consensus (RANSAC). Experimental results of system shows that both modules perform robustly with less computational load and that it can be a reliable intelligent vehicle system.

[1]  Dongil Han,et al.  Design and VLSI implementation of high-performance face-detection engine for mobile applications , 2011, 2011 IEEE International Conference on Consumer Electronics (ICCE).

[2]  Takashi Naito,et al.  Lane detection with roadside structure using on-board monocular camera , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[3]  Andreas Ernst,et al.  Face detection with the modified census transform , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[4]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[5]  Liang-Gee Chen,et al.  An intelligent vision-based vehicle detection and tracking system for automotive applications , 2011, 2011 IEEE International Conference on Consumer Electronics (ICCE).

[6]  A. Lopez,et al.  Detection of lane markings based on ridgeness and RANSAC , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[7]  Zehang Sun,et al.  On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.