Extending the stixel world using polynomial ground manifold approximation

Saleem, Noor Haitham; Rezaei, Mahdi; Klette, Reinhard

doi:10.1109/m2vip.2017.8211440

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…by using a linear Least-Squares (LLS) method. For example, Saleem et al [4] use a polynomial representation to fit a ground surface on v-disparity estimates of stereo cameras.…”

Section: R Wmentioning

confidence: 99%

Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines

Wirges¹,

Rösch²,

Bieder³

et al. 2022

Preprint

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We propose a fast and robust method to estimate the ground surface from LiDAR measurements on an automated vehicle. The ground surface is modeled as a uniform Bspline which is robust towards varying measurement densities and with a single parameter controlling the smoothness prior. We model the estimation process as a robust Least-Squares optimization problem which can be reformulated as a linear problem and thus solved efficiently. Using the SemanticKITTI data set, we conduct a quantitative evaluation by classifying the point-wise semantic annotations into ground and non-ground points. Finally, we validate the approach on our research vehicle in real-world scenarios.

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“…by using a linear Least-Squares (LLS) method. For example, Saleem et al [4] use a polynomial representation to fit a ground surface on v-disparity estimates of stereo cameras.…”

Section: R Wmentioning

confidence: 99%

Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines

Wirges¹,

Rösch²,

Bieder³

et al. 2022

Preprint

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show abstract

“…A variety of methods has been proposed in literature [5], [7], [10], [20], [42] to obtain map G. Some methods directly work on raw data, such as image intensities, disparities, or 3D points, while others apply data projections to reduce the dimensionality of the raw data. Direct methods and projectionbased methods are reviewed in this section.…”

Section: Ground Manifold Modellingmentioning

confidence: 99%

“…Such a scheme suffers from shortages highlighted in [6]. Accordingly we selected four recently discussed models (plane-fit and line-fit [43], poly-fit [42], and graph-cut [47]) which are mainly dependent on the v-disparity space. This brings numerous advantages for ground-manifold detection.…”

Section: Multiocular Visionmentioning

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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“…Nevertheless, the number of tested images was not sufficient to statistically validate the claimed accuracy. Cascade of Classifiers [15], Stereo Vision based features [31], [32], and Fuzzy logic Neural Networks [27] are other alternative approaches.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Method for License Plate Localization Using Multiple Statistical Features in a Multilayer Perceptron Neural Network

Rezaei

Isehaghi

2018

2018 9th Conference on Artificial Intelligence and Robotics and 2nd Asia-Pacific International Symposium

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Accurate license plate localization is the most important prerequisite in ANPR (Automatic Number Plate Recognition) systems. Majority of the existing algorithms use a single feature to obtain the license plate location which causes to potential false detections. In this article we propose a robust methodology using 16 statistical features while we still preserve real-time processing of the system which is a requirement for such applications. The proposed method uses a Vertical Projection technique and Discrete Fourier Transform (DFT) in order to extract multiple statistical features, as well as K-means clustering and multilayer perceptron neural network technique to identify the location of a license plate in an image. The method is compared with the state-of-the-art research in the field and the effectiveness of the method is evaluated for various types of license plates with different scripts.

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Extending the stixel world using polynomial ground manifold approximation

Cited by 6 publications

References 35 publications

Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines

Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines

Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

An Efficient Method for License Plate Localization Using Multiple Statistical Features in a Multilayer Perceptron Neural Network

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