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Superpixel-based Vehicle Detection using Plane Normal Vector in Dispar ity Space
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 Title & Authors
Superpixel-based Vehicle Detection using Plane Normal Vector in Dispar ity Space
Seo, Jeonghyun; Sohn, Kwanghoon;
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This paper proposes a framework of superpixel-based vehicle detection method using plane normal vector in disparity space. We utilize two common factors for detecting vehicles: Hypothesis Generation (HG) and Hypothesis Verification (HV). At the stage of HG, we set the regions of interest (ROI) by estimating the lane, and track them to reduce computational cost of the overall processes. The image is then divided into compact superpixels, each of which is viewed as a plane composed of the normal vector in disparity space. After that, the representative normal vector is computed at a superpixel-level, which alleviates the well-known problems of conventional color-based and depth-based approaches. Based on the assumption that the central-bottom of the input image is always on the navigable region, the road and obstacle candidates are simultaneously extracted by the plane normal vectors obtained from K-means algorithm. At the stage of HV, the separated obstacle candidates are verified by employing HOG and SVM as for a feature and classifying function, respectively. To achieve this, we trained SVM classifier by HOG features of KITTI training dataset. The experimental results demonstrate that the proposed vehicle detection system outperforms the conventional HOG-based methods qualitatively and quantitatively.
Normal Vector Computation;Stereo Matching;Superpixel Segmentation;Free Space Estimation;Vehicle Detection;
 Cited by
Multi-spectral Vehicle Detection based on Convolutional Neural Network, Journal of Korea Multimedia Society, 2016, 19, 12, 1909  crossref(new windwow)
Volvo Trucks, European Accident Research and Safety Report, 2013.

P. Parodi and G. Piccioli, “A Feature-based Recognition Scheme for Traffic Scenes,” Proceeding of IEEE Intelligent Vehicle Symposium, pp. 229-234, 1995.

A. Bensrhair, M. Bertozzi, and A. Broggi, “A Cooperative Approach to Vision-based Vehicle Detection,” Proceeding of IEEE Intelligent Transportation Systems, pp. 207-212, 2001.

V.N. Vapnik, The Nature of Statistical Learning Theory, Springer, NewYork, 1995.

Y. Freund and R.E. Schapire, “A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting,” Proceeding of Conference Computational Learning Theory, pp. 23-37, 1995.

N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proceeding of IEEE Computer Vision and Pattern Recognition, pp. 886-893, 2005.

S. Zehang, G. Bebis, and R. Miller, “On-road Vehicle Detection Using Gabor Filters and Support Vector Machines,” Proceeding of International Conference Digital Signal Processing, Vol. 2, pp. 1019-1022, 2002.

Q. Truong and B. Lee, “Vehicle Detection Algorithm Using Hypothesis Generation and Verification,” Emerging Intelligent Computing Technology and Applications, pp. 534-543, 2009.

L. Mao, M. Xie, Y. Huang, and Y. Zhang, “Preceding Vehicle Detection Using Histograms of Oriented Gradients,” Proceeding of IEEE International Conference Communications, Circuits and Systems, pp. 354-358, 2010.

S.E. Grigorescu, N. Petkov, and P. Kruizinga, “Comparison of Texture Features Based on Gabor Filters,” IEEE Transactions on Image Processing, Vol. 11, No. 10, pp. 1160-1167, 2002. crossref(new window)

S. Zehang, G. Bebis, and R. Miller, “On-road Vehicle Detection Using Evolutionary Gabor Filter Optimization,” IEEE Transactions on Intelligent Transportation Systems, Vol. 6, No. 2, pp. 125-137, 2005. crossref(new window)

H. Cheng, N. Zheng, and C. Sun, “Boosted Gabor Features Applied to Vehicle Detection,” IEEE Proceeding of International Conference Pattern Recognition, pp. 662-666, 2006.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “SLIC Superpixels Compared to State-of-the-art Superpixel Methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 11, pp. 2274-2282, 2012. crossref(new window)

H. Hirschmuller, “Accurate and Efficient Stereo Processing by Semiglobal Matching and Mutual Information,” Proceeding of IEEE Conference Computer Vision and Pattern Recognition, Vol. 2, pp. 807-814, 2005.

T. Kanungo, D.M. Mount, N.S. Netanyahu, C.D. Piatko, R. Silverman, and A.Y. Wu, “An Efficient k-Means Clustering Algorithm: Analysis and Implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 881-892, 2002. crossref(new window)

T. Malisiewicz, A. Gupta, and A.A. Efros, "Ensemble of Exemplar-svms for Object Detection and Beyond," Proceeding of IEEE International Conference Computer Vision, pp. 89-96, 2011.

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. “Vision Meets Robotics: The KITTI Dataset,” The International J. of Robotics Research, 2013.

K.K. Sung and T. Poggio, “Example-based Learning for View-based Human Face Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, pp. 39-51, 1998. crossref(new window)

N. Dalal, B. Triggs, and C. Schmid, “Human Detection Using Oriented Histograms of Flow and Appearance,” Proceeding of European Conference on Computer Vision, pp. 428-441, 2006.

M.S. Choi, J.H. Lee, J.H. Suk, T.M. Roh, and J.C. Shim, “Vehicle Detection based on the Haar-like feature and Image Segmentation, “ Journal of Korea Multimedia Society, 13(9), 1314-1321, 2010.