A Reliable Method for Detecting Road Regions from a Single Image Based on Color Distribution and Vanishing Point Location

John, Neethu; Anusha, B.; Kutty, Krishnan

doi:10.1016/j.procs.2015.08.002

Cited by 25 publications

(8 citation statements)

References 21 publications

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“…Another road status classification research [20] focused on sensors installed under the road surface, while some works [21], [22], and [23] have used a support vector machine (SVM) and the K-nearest neighbor (KNN) to determine road conditions. For road region detection, [24] and [25] recognized drivable areas of the road using a classical image processing technique based on vanishing point detection.…”

Section: Related Workmentioning

confidence: 99%

Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios

et al. 2021

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Section: Related Workmentioning

confidence: 99%

Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios

et al. 2021

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“…One straightforward paradigm detects the road regions by extracting the lane markings and road boundaries. This category of approaches utilizes conventional image processing techniques to extract the road boundaries by using image features such as edge, position and color [4,5,6,7,8,9]. After that, the boundaries are fitted (linear models [6,7] and high-order curves/splines are used for straight and curved road segments [8], respectively), and the area between the identified lane markings and/or boundaries is considered as the drivable road region.…”

Section: Related Workmentioning

confidence: 99%

“…For camera-based perception systems, the objective of drivable road region detection is to identify a region of pixels that indicates the drivable (or navigation-free) area in a given image captured by the camera, and examples of applications of camera in road/lane detection can be found in [4,5,6,7,8,9]. The real-world autonomous driving scenarios pose significant challenges to camera-based perception systems, due to the following factors: (1) Unstructured road environments: road markings and lane borders are not always available, and the marking/borders may be too vague to be identified; (2) variable illumination conditions: the images may contain shadows and other undesirable illumination conditions; (3) road curvatures: the camera’s field-of-view may not capture the entire region needed due to curved road segments; (4) ununiform pavement appearance and occlusions: the road pavement in the image may contain variable texture and color, and the objects in the camera’s field-of-view may cause occlusions in the image.…”

Section: Introductionmentioning

confidence: 99%

Robust Drivable Road Region Detection for Fixed-Route Autonomous Vehicles Using Map-Fusion Images

Cai

Zhou

et al. 2018

Sensors

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Environment perception is one of the major issues in autonomous driving systems. In particular, effective and robust drivable road region detection still remains a challenge to be addressed for autonomous vehicles in multi-lane roads, intersections and unstructured road environments. In this paper, a computer vision and neural networks-based drivable road region detection approach is proposed for fixed-route autonomous vehicles (e.g., shuttles, buses and other vehicles operating on fixed routes), using a vehicle-mounted camera, route map and real-time vehicle location. The key idea of the proposed approach is to fuse an image with its corresponding local route map to obtain the map-fusion image (MFI) where the information of the image and route map act as complementary to each other. The information of the image can be utilized in road regions with rich features, while local route map acts as critical heuristics that enable robust drivable road region detection in areas without clear lane marking or borders. A neural network model constructed upon the Convolutional Neural Networks (CNNs), namely FCN-VGG16, is utilized to extract the drivable road region from the fused MFI. The proposed approach is validated using real-world driving scenario videos captured by an industrial camera mounted on a testing vehicle. Experiments demonstrate that the proposed approach outperforms the conventional approach which uses non-fused images in terms of detection accuracy and robustness, and it achieves desirable robustness against undesirable illumination conditions and pavement appearance, as well as projection and map-fusion errors.

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“…State-of-the-art methods for VP estimation can be categorised into three classifications: algorithms aiming to estimate a single VP, like the work of [9][10][11][12]; three orthogonal VP as in [7,13,14]; or any possible non-orthogonal VP as done in [15]. While some methods require knowledge of camera calibration, others operate in a non-calibrated setting.…”

Section: Introductionmentioning

confidence: 99%

Vanishing point detection using the teaching learning‐based optimisation algorithm

Lopez-Martinez

Cuevas

2020

IET image process

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Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population‐based method: The Teaching Learning Based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching–learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1°.

show abstract

A Reliable Method for Detecting Road Regions from a Single Image Based on Color Distribution and Vanishing Point Location

Cited by 25 publications

References 21 publications

Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios

Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios

Robust Drivable Road Region Detection for Fixed-Route Autonomous Vehicles Using Map-Fusion Images

Vanishing point detection using the teaching learning‐based optimisation algorithm

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