Radar-Vision Based Vehicle Recognition with Evolutionary Optimized and Boosted Features

We present a real-time monocular vehicle detection and recognition system for driver assistance based on the fusion of data from a radar and a video sensor. The radar data is used both for narrowing down the size of the search area for vehicle rears on the video image and for the distance measurement of the vehicles in front. Using the passive video sensor a radar object is verified and the width as well as the lateral position of the vehicle are determined. The contribution of this work is threefold: At first, we present and apply a methodology for developing a novel evolutionary optimized symmetry measure. Secondly, we demonstrate a vehicle detection and recognition algorithm consisting of two steps: hypothesis generation using a detector based on a set of Haar-like filters and an AdaBoost learning algorithm and hypothesis verification using an evolutionary optimized and biologically motivated vehicle recognition system. Finally, the performance of both the individual components and the complete vehicle detection and recognition system is evaluated by not only using classical confusion matrices but also giving information on the accuracy of the width and lateral position sensing. Our experimental results demonstrate a robust and real-time system trained and tested on more than 30,000 images.

[1]  Massimo Bertozzi,et al.  Stereo vision-based vehicle detection , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[2]  W. Seelen,et al.  Intensity and edge-based symmetry detection with an application to car-following , 1993 .

[3]  Takeo Kanade,et al.  Car Recognition for the CMU Navlab , 1990 .

[4]  Heiko Wersing,et al.  Evolutionary optimization of a hierarchical object recognition model , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  M. Dianati,et al.  An Introduction to Genetic Algorithms and Evolution , 2002 .

[6]  Georg Schneider.  Evolutionäre Optimierung eines biologisch motivierten visuellen Objekterkennungssystems , 2004 .

[7]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[8]  Pietro Cerri,et al.  Radar-vision fusion for vehicle detection , 2006 .

[9]  Aurelio Piazzi,et al.  Visual perception of obstacles and vehicles for platooning , 2000, IEEE Trans. Intell. Transp. Syst..

[10]  Tomaso A. Poggio,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.

[11]  Andreas Kuehnle,et al.  Symmetry-based recognition of vehicle rears , 1991, Pattern Recognit. Lett..

[12]  A. Lopez,et al.  3D vehicle sensor based on monocular vision , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[13]  A. Broggi,et al.  A cooperative approach to vision-based vehicle detection , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[14]  Ernst D. Dickmanns,et al.  Obstacle Detection, Tracking And State Estimation For Autonomous Road Vehicle Guidance , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  P. C. Antonello,et al.  Multi-resolution vehicle detection using artificial vision , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[16]  Zehang Sun,et al.  On-road vehicle detection using Gabor filters and support vector machines , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

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