Autonomous vehicle detection system using visible and infrared camera

This paper presents a vision-based vehicle detection system in the infrared (IR) and vision system using an effective feature extraction and algorithm. This system follows two steps: Hypothesis Generation (HG) method and Hypothesis Verification (HV) method. In HG method, vertical and horizontal edges are used. To extract these edges effectively a neighborhood gradient prediction(NGP) edge detection is used. With these extracted edges, the vehicle location candidates are generated. This step reduces the computational time in comparison with exhaustive search method. In HV method, the effective feature extraction such as HOG and GABOR feature are used. A support vector machine (SVM) for classification is also used. This step verifies if the vehicle candidates are vehicle or not. The test image is obtained by a monocular IR camera and visible camera attached on moving vehicle. In vision image, NGP method is compared with SOBEL method. In IR image, HOG feature is compared with GABOR feature.

[1]  W. Kruger,et al.  Real-time estimation and tracking of optical flow vectors for obstacle detection , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[2]  Zehang Sun,et al.  A real-time precrash vehicle detection system , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..

[3]  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.

[4]  Seiichi Mita,et al.  Vision-based vehicle detection for nighttime with discriminately trained mixture of weighted deformable part models , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[5]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Jae-Soo Cho,et al.  Vision-based vehicle detection and inter-vehicle distance estimation for driver alarm system , 2012, 2012 12th International Conference on Control, Automation and Systems.

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

[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]  Zehang Sun,et al.  Monocular precrash vehicle detection: features and classifiers , 2006, IEEE Transactions on Image Processing.

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

[11]  Wei Luo,et al.  Vehicle capturing and counting using a new edge extraction approach , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

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