Monocular precrash vehicle detection: features and classifiers

Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.

[1]  Massimo Bertozzi,et al.  GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection , 1998, IEEE Trans. Image Process..

[2]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[3]  Bruce A. Draper,et al.  Color machine vision for autonomous vehicles , 1998 .

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

[5]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[7]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

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

[9]  Georgios Tziritas,et al.  Wavelet packet analysis for face recognition , 2000, Image Vis. Comput..

[10]  Zehang Sun,et al.  Quantized wavelet features and support vector machines for on-road vehicle detection , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[11]  Takeo Kanade,et al.  Probabilistic modeling of local appearance and spatial relationships for object recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[12]  William E. Higgins,et al.  Efficient Gabor filter design for texture segmentation , 1996, Pattern Recognit..

[13]  Zehang Sun,et al.  Improving the performance of on-road vehicle detection by combining Gabor and wavelet features , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  Zehang Sun,et al.  Boosting object detection using feature selection , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

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

[17]  George Bebis,et al.  Neural-network-based gender classification using genetic search for eigen-feature selection , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[18]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[19]  E. D. Dickmanns,et al.  The development of machine vision for road vehicles in the last decade , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[20]  Dirk Dickmanns,et al.  Multiple object recognition and scene interpretation for autonomous road vehicle guidance , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[21]  Christian Goerick,et al.  Artificial neural networks in real-time car detection and tracking applications , 1996, Pattern Recognit. Lett..

[22]  David Salesin,et al.  Fast multiresolution image querying , 1995, SIGGRAPH.

[23]  Ernst D. Dickmanns,et al.  An integrated spatio-temporal approach to automatic visual guidance of autonomous vehicles , 1990, IEEE Trans. Syst. Man Cybern..

[24]  Ryuzo Okada,et al.  Obstacle detection using projective invariant and vanishing lines , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[25]  Sushil J. Louis,et al.  Genetic feature subset selection for gender classification: a comparison study , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..

[26]  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).

[27]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[28]  S. Ogata,et al.  A stereo-based vehicle detection method under windy conditions , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[29]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[30]  Zehang Sun,et al.  Evolutionary Gabor filter optimization with application to vehicle detection , 2003, Third IEEE International Conference on Data Mining.

[31]  D Lang,et al.  Building safer cars , 2001 .

[32]  Geoffrey D. Sullivan,et al.  A Generic Deformable Model for Vehicle Recognition , 1995, BMVC.

[33]  David J. Crisp,et al.  Uniqueness of the SVM Solution , 1999, NIPS.

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

[35]  Thomas Kalinke,et al.  An image processing system for driver assistance , 2000, Image Vis. Comput..

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

[37]  Hans-Hellmut Nagel,et al.  Algorithmic characterization of vehicle trajectories from image sequences by motion verbs , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[38]  J. Little,et al.  Inverse perspective mapping simplifies optical flow computation and obstacle detection , 2004, Biological Cybernetics.

[39]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[40]  Nello Cristianini,et al.  Editorial: Kernel Methods: Current Research and Future Directions , 2002, Machine-mediated learning.

[41]  Takeo Kanade,et al.  Rotation Invariant Neural Network-Based Face Detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[42]  Massimo Bertozzi,et al.  Artificial vision in road vehicles , 2002, Proc. IEEE.

[43]  W. D. Jones.  Building safer cars , 2002 .

[44]  Takeo Kanade,et al.  A statistical approach to 3d object detection applied to faces and cars , 2000 .

[45]  Larry S. Davis,et al.  Multiple vehicle detection and tracking in hard real-time , 1996, Proceedings of Conference on Intelligent Vehicles.

[46]  Reinhold Behringer,et al.  The seeing passenger car 'VaMoRs-P' , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[47]  David G. Stork,et al.  Pattern Classification , 1973 .

[48]  Pietro Perona,et al.  Unsupervised Learning of Models for Recognition , 2000, ECCV.

[49]  M. Georgiopoulos,et al.  Feed-forward neural networks , 1994, IEEE Potentials.

[50]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[51]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[53]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[54]  Michael Georgiopoulos,et al.  Face Detection and Verification Using Genetic Search , 2000, Int. J. Artif. Intell. Tools.

[55]  Chris J. Harris,et al.  VEHICLE DETECTION AND RECOGNITION IN GREYSCALE IMAGERY , 1995 .

[56]  Thomas Kalinke,et al.  A Texture-based Object Detection and an adaptive Model-based Classification , 1998 .

[57]  W. D. Jones,et al.  Keeping cars from crashing , 2001 .

[58]  W. Ritter,et al.  Obstacle detection based on color blob flow , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[59]  Rolf P. Würtz,et al.  Object Recognition Robust Under Translations, Deformations, and Changes in Background , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[60]  B. Michaelis,et al.  Robust vehicle detection at large distance using low resolution cameras , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[61]  S. Yuta,et al.  Obstacle Detection By Vision System For An Autonomous Vehicle , 1993, Proceedings of the Intelligent Vehicles '93 Symposium.