Dense Stereo-Based Robust Vertical Road Profile Estimation Using Hough Transform and Dynamic Programming

Suhr, Jae Kyu; Jung, Ho Gi

doi:10.1109/tits.2014.2369002

Cited by 21 publications

(9 citation statements)

References 30 publications

(92 reference statements)

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“…erefore, the traditional Hough transform [32] and RANSAC [33] straight line fitting can no longer accurately reflect the road information. We combine the final V view and horizon position obtained by the considerate U-V view method, as shown in Figure 5(b).…”

Section: Multiple Traffic Object Classification Detectionmentioning

confidence: 99%

Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework

Song

Yao

et al. 2020

Complexity

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In order to solve the problems of traffic object detection, fuzzification, and simplification in real traffic environment, an automatic detection and classification algorithm for roads, vehicles, and pedestrians with multiple traffic objects under the same framework is proposed. We construct the final V view through a considerate U-V view method, which determines the location of the horizon and the initial contour of the road. Road detection results are obtained through error label reclassification, omitting point reassignment, and so an. We propose a peripheral envelope algorithm to determine sources of vehicles and pedestrians on the road. The initial segmentation results are determined by the regional growth of the source point through the minimum neighbor similarity algorithm. Vehicle detection results on the road are confirmed by combining disparity and color energy minimum algorithms with the object window aspect ratio threshold method. A method of multifeature fusion is presented to obtain the pedestrian target area, and the pedestrian detection results on the road are accurately segmented by combining the disparity neighbor similarity and the minimum energy algorithm. The algorithm is tested in three datasets of Enpeda, KITTI, and Daimler; then, the corresponding results prove the efficiency and accuracy of the proposed approach. Meanwhile, the real-time analysis of the algorithm is performed, and the average time efficiency is 13 pfs, which can realize the real-time performance of the detection process.

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Section: Multiple Traffic Object Classification Detectionmentioning

confidence: 99%

Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework

Song

Yao

et al. 2020

Complexity

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“…After obtaining occlusion or error regions, these areas are refined, and the disparity is estimated using DP and an edge preserving noise filter. Apart from this, Suhr, et al [67] proposed a robust stereo vision with a combination of DP and Hough Transform to measure the vertical road profile. The results indicate that the proposed algorithm can estimate the road profile, even if the road is distant from the camera.…”

Section: Dynamic Programming (Dp)mentioning

confidence: 99%

A Review on Stereo Vision Algorithm: Challenges and Solutions

Kok

Rajendran²

2020

ECTI-CIT

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This paper presents a survey on existing stereo vision algorithms. The existing stereo vision algorithms are discussed in terms of concept, performance and related improvements. Also, a brief analysis of performance comparison among existing stereo vision algorithms is presented. Moreover, available improvements and solutions for stereo vision challenges such as computational complexity, occlusion, radiometric distortion, depth discontinuity and textureless region are reviewed.

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“…It is challenging to identify an "ideal" curve in v-disparity space using a curve-approximation method and v-disparity for binocular vision alone. Stereo vision supports the use of techniques such as v-disparity representation [5], disparity analysis [31], or occupancy grid generation [2], [32].…”

Section: Related Workmentioning

confidence: 99%

“…The previously presented line fitting method can only handle planar road surfaces [31]. For a non-flat road geometry, the vdisparity map shows a curved distribution of pixel disparities.…”

Section: Curve Fittingmentioning

confidence: 99%

Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

Saleem

Chien

Rezaei

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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This paper highlights the role of ground manifold modelling for stixel calculations; stixels are medium-level data representations used for the development of computer vision modules for self-driving cars. By using single disparity maps and simplifying ground manifold models, calculated stixels may suffer from noise, inconsistency, and false-detection rates for obstacles, especially in challenging datasets. Stixel calculations can be improved with respect to accuracy and robustness by using more adaptive ground manifold approximations. A comparative study of stixel results, obtained for different groundmanifold models (e.g. plane-fitting, line-fitting in v-disparities or polynomial approximation, and graph cut), defines the main part of this paper. The paper also considers the use of trinocular stereo vision and shows that this provides options to enhance stixel results compared to binocular recording. Comprehensive experiments are performed on two publicly available challenging datasets. We also use a novel way for comparing calculated stixels with ground truth. We compare depth information, as given by extracted stixels, with ground-truth depth, provided by depth measurements using a highly accurate LiDAR range sensor (as available in one of the public datasets). We evaluate the accuracy of four different ground-manifold methods. Experimental results also include quantitative evaluations of the trade-off between accuracy and run time. As a result, the proposed trinocular recording together with graph-cut estimation of ground manifolds appears to be a recommended way, also considering challenging weather and lighting conditions.

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Dense Stereo-Based Robust Vertical Road Profile Estimation Using Hough Transform and Dynamic Programming

Cited by 21 publications

References 30 publications

Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework

Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework

A Review on Stereo Vision Algorithm: Challenges and Solutions

Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

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