Vehicle Detection Algorithm Using Hypothesis Generation and Verification

Truong, Quoc Bao; Lee, Byung Ryong

doi:10.1007/978-3-642-04070-2_59

Cited by 17 publications

(10 citation statements)

References 2 publications

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“…Truong et al [151] performed PCA to build a feature vector for vehicle, naming it 'Eigen space of vehicle'. An SVM classifier was adopted for classification which resulted in 95% detection rate.…”

Section: B Verification Stagementioning

confidence: 99%

See 1 more Smart Citation

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

Mukhtar

Xia

Tang

2015

IEEE Trans. Intell. Transport. Syst.

375

202

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Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.

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Section: B Verification Stagementioning

confidence: 99%

“…A fine set of features should capture most of the variability in the appearance of a vehicle [146]. Numerous features for vehicle classification have been proposed in literature including Histogram of Oriented Gradient (HOG) [147], [148], Gabor [149], Principal Component Analysis (PCA) [150], [151] and Haar wavelets [152], [153].…”

Section: B Verification Stagementioning

confidence: 99%

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

Mukhtar

Xia

Tang

2015

IEEE Trans. Intell. Transport. Syst.

375

202

View full text Add to dashboard Cite

show abstract

“…A fine set of features should capture most of the variability in the appearance of a vehicle. Numerous features for vehicle classification have been proposed such as HOG [6], Gabor [7], Principal Component Analysis (PCA) [8].…”

Section: Related Workmentioning

confidence: 99%

Superpixel-based Vehicle Detection using Plane Normal Vector in Dispar ity Space

Seo¹,

Sohn²

2016

Journal of Korea Multimedia Society

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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.

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“…employing the Bayes rule assuming Gaussian distributions) [28,29,30]. In [31], Principle Component Analysis (PCA) was used for feature extraction and linear Support Vector Machine (SVM) for classification of vehicle images. Goerick et al [32] employed Local Orientation Code (LOC) to extract the edge information of ROI and NNs to learn the characteristics of vehicles.…”

Section: Introductionmentioning

confidence: 99%