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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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 Abstract
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.
 Keywords
Normal Vector Computation;Stereo Matching;Superpixel Segmentation;Free Space Estimation;Vehicle Detection;
 Language
English
 Cited by
1.
Multi-spectral Vehicle Detection based on Convolutional Neural Network,;;;;

한국멀티미디어학회논문지, 2016. vol.19. 12, pp.1909-1918 crossref(new window)
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Multi-spectral Vehicle Detection based on Convolutional Neural Network, Journal of Korea Multimedia Society, 2016, 19, 12, 1909  crossref(new windwow)
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