Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine) model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment.

[1]  Dipti Patra,et al.  Combining GLCM Features and Markov Random Field Model for Colour Textured Image Segmentation , 2011, 2011 International Conference on Devices and Communications (ICDeCom).

[2]  Baozhen Yao,et al.  An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot , 2016, Ann. Oper. Res..

[3]  Xiaoyan Wang,et al.  Three-Frame Difference Algorithm Research Based on Mathematical Morphology , 2012 .

[4]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[5]  Jan Flusser,et al.  Affine moment invariants: a new tool for character recognition , 1994, Pattern Recognit. Lett..

[6]  S. M. Mahbubur Rahman,et al.  Detection and Classification of Vehicles From Video Using Multiple Time-Spatial Images , 2012, IEEE Transactions on Intelligent Transportation Systems.

[7]  Chuntian Cheng,et al.  Optimizing the distribution of shopping centers with parallel genetic algorithm , 2007, Eng. Appl. Artif. Intell..

[8]  Tae Yong Kim,et al.  A novel signal processing technique for vehicle detection radar , 2003, IEEE MTT-S International Microwave Symposium Digest, 2003.

[9]  Baozhen Yao,et al.  A support vector machine with the tabu search algorithm for freeway incident detection , 2014, Int. J. Appl. Math. Comput. Sci..

[10]  O. Mano,et al.  Forward collision warning with a single camera , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[11]  Erdinç Altug,et al.  Increasing driving safety with a multiple vehicle detection and tracking system using ongoing vehicle shadow information , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[12]  Mohan M. Trivedi,et al.  Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis , 2013, IEEE Transactions on Intelligent Transportation Systems.

[13]  Edward Jones,et al.  Rear-Lamp Vehicle Detection and Tracking in Low-Exposure Color Video for Night Conditions , 2010, IEEE Transactions on Intelligent Transportation Systems.

[14]  Wei Zhan,et al.  Algorithm Research on Moving Vehicles Detection , 2011 .

[15]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[16]  Whoi-Yul Kim,et al.  Forward vehicle detection using cluster-based AdaBoost , 2014 .

[17]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[18]  B. Yao,et al.  Improved support vector machine regression in multi-step-ahead prediction for rock displacement surrounding a tunnel , 2014 .

[19]  Baozhen Yao,et al.  Improved Support Vector Machine Regression in Multi-Step-Ahead Prediction for Tunnel Surrounding Rock Displacement , 2014 .

[20]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[21]  Gangyao Kuang,et al.  Modified two-dimensional otsu image segmentation algorithm and fast realisation , 2012 .

[22]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[23]  Nan Wang,et al.  Rear Vehicle Detection and Tracking for Lane Change Assist , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[24]  Chieh-Chih Wang,et al.  LADAR-based detection and tracking of moving objects from a ground vehicle at high speeds , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[25]  Chris T. Kiranoudis,et al.  A background subtraction algorithm for detecting and tracking vehicles , 2011, Expert Syst. Appl..

[26]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[27]  Kang-Hyun Jo,et al.  Vehicle detection using tail light segmentation , 2011, Proceedings of 2011 6th International Forum on Strategic Technology.