General Road Detection From a Single Image
01 Aug 2010-IEEE Transactions on Image Processing (IEEE Trans Image Process)-Vol. 19, Iss: 8, pp 2211-2220
TL;DR: This paper decomposes the road detection process into two steps: the estimation of the vanishing point associated with the main (straight) part of the road, followed by the segmentation of the corresponding road area based upon the detected vanishing point.
Abstract: Given a single image of an arbitrary road, that may not be well-paved, or have clearly delineated edges, or some a priori known color or texture distribution, is it possible for a computer to find this road? This paper addresses this question by decomposing the road detection process into two steps: the estimation of the vanishing point associated with the main (straight) part of the road, followed by the segmentation of the corresponding road area based upon the detected vanishing point. The main technical contributions of the proposed approach are a novel adaptive soft voting scheme based upon a local voting region using high-confidence voters, whose texture orientations are computed using Gabor filters, and a new vanishing-point-constrained edge detection technique for detecting road boundaries. The proposed method has been implemented, and experiments with 1003 general road images demonstrate that it is effective at detecting road regions in challenging conditions.
Citations
More filters
TL;DR: The proposed gLoG-based blob detector can accurately detect the centers and estimate the sizes and orientations of cell nuclei, and can produce promising estimation of texture orientations, achieving an accurate texture-based road vanishing point detection method.
Abstract: In this paper, we propose a generalized Laplacian of Gaussian (LoG) (gLoG) filter for detecting general elliptical blob structures in images. The gLoG filter can not only accurately locate the blob centers but also estimate the scales, shapes, and orientations of the detected blobs. These functions can be realized by generalizing the common 3-D LoG scale-space blob detector to a 5-D gLoG scale-space one, where the five parameters are image-domain coordinates (x, y), scales (σx, σy), and orientation (θ), respectively. Instead of searching the local extrema of the image's 5-D gLoG scale space for locating blobs, a more feasible solution is given by locating the local maxima of an intermediate map, which is obtained by aggregating the log-scale-normalized convolution responses of each individual gLoG filter. The proposed gLoG-based blob detector is applied to both biomedical images and natural ones such as general road-scene images. For the biomedical applications on pathological and fluorescent microscopic images, the gLoG blob detector can accurately detect the centers and estimate the sizes and orientations of cell nuclei. These centers are utilized as markers for a watershed-based touching-cell splitting method to split touching nuclei and counting cells in segmentation-free images. For the application on road images, the proposed detector can produce promising estimation of texture orientations, achieving an accurate texture-based road vanishing point detection method. The implementation of our method is quite straightforward due to a very small number of tunable parameters.
254 citations
Cites methods from "General Road Detection From a Singl..."
...The six statistics are obtained with the same parameter settings: the soft-voting scheme as proposed in [23] and [24]; however, instead of using a highly confident region as voting area, the voting region for our experiments is set to be the local...
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...[23], [24] once texture orientations are given....
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...[23], [24] to detect road vanishing point and compare with the Gabor-filter-based method (36 orientations used in Gabor filters) [23], [24] for vanishing point detection....
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...The first vanishing point voting map is obtained based on this partial orientation map according to [23], [24], shown as the fifth image of the top row....
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TL;DR: A graph-cut-based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas.
Abstract: An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground–vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut–based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 $\times$ 595 videos on average.
198 citations
TL;DR: This paper proposes a real-time and illumination invariant lane detection method for lane departure warning system that works well in various illumination conditions such as in bad weather conditions and at night time.
Abstract: Invariant property of lane color under various illuminations is utilized for lane detection.Computational complexity is reduced using vanishing point detection and adaptive ROI.Datasets for evaluation include various environments from several devices.Simulation demo demonstrate fast and powerful performance for real-time applications. Lane detection is an important element in improving driving safety. In this paper, we propose a real-time and illumination invariant lane detection method for lane departure warning system. The proposed method works well in various illumination conditions such as in bad weather conditions and at night time. It includes three major components: First, we detect a vanishing point based on a voting map and define an adaptive region of interest (ROI) to reduce computational complexity. Second, we utilize the distinct property of lane colors to achieve illumination invariant lane marker candidate detection. Finally, we find the main lane using a clustering method from the lane marker candidates. In case of lane departure situation, our system sends driver alarm signal. Experimental results show satisfactory performance with an average detection rate of 93% under various illumination conditions. Moreover, the overall process takes only 33ms per frame.
194 citations
TL;DR: This paper proposes a novel methodology based on image texture analysis for the fast estimation of the vanishing point in challenging and unstructured roads that uses joint activities of only four Gabor filters to precisely estimate the local dominant orientation at each pixel location in the image plane.
Abstract: Vision-based road detection in unstructured environments is a challenging problem as there are hardly any discernible and invariant features that can characterize the road or its boundaries in such environments. However, a salient and consistent feature of most roads or tracks regardless of type of the environments is that their edges, boundaries, and even ruts and tire tracks left by previous vehicles on the path appear to converge into a single point known as the vanishing point. Hence, estimating this vanishing point plays a pivotal role in the determination of the direction of the road. In this paper, we propose a novel methodology based on image texture analysis for the fast estimation of the vanishing point in challenging and unstructured roads. The key attributes of the methodology consist of the optimal local dominant orientation method that uses joint activities of only four Gabor filters to precisely estimate the local dominant orientation at each pixel location in the image plane, the weighting of each pixel based on its dominant orientation, and an adaptive distance-based voting scheme for the estimation of the vanishing point. A series of quantitative and qualitative analyses are presented using natural data sets from the Defense Advanced Research Projects Agency Grand Challenge projects to demonstrate the effectiveness and the accuracy of the proposed methodology.
146 citations
Cites background or methods from "General Road Detection From a Singl..."
...number of orientations to achieve a precise angular resolution for the local dominant orientation ( in [4] and in [5], [6])....
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...method [6] shows 56 (11%) images with large error, which is better than the Rasmussen method [5], while its performance is degraded in the small error portions of the histogram (i....
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...The vanishing-point estimation error was measured by comparing the result of the algorithms against the vanishing-point ground truth manually determined through human perspective perception [4]–[6]....
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...method [6] occurs in only 63 (12%) images....
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...Texture-based approaches apply a bank of oriented filters such as Gabor filter banks [4]–[6] or steerable filter banks [7], [14] and choose the orientation corresponding to the maximum filter response as the dominant texture orientation at each pixel location ....
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TL;DR: An algorithm to estimate road priors online using geographical information is proposed, providing relevant initial information about the road location, and contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry are used.
Abstract: Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning. Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.
115 citations
Cites background from "General Road Detection From a Singl..."
...For instance, a common approach consists of using lane markings [8]–[10] for structured roads or road boundaries [11] for general roads....
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References
More filters
TL;DR: The authors present an efficient architecture to synthesize filters of arbitrary orientations from linear combinations of basis filters, allowing one to adaptively steer a filter to any orientation, and to determine analytically the filter output as a function of orientation.
Abstract: The authors present an efficient architecture to synthesize filters of arbitrary orientations from linear combinations of basis filters, allowing one to adaptively steer a filter to any orientation, and to determine analytically the filter output as a function of orientation. Steerable filters may be designed in quadrature pairs to allow adaptive control over phase as well as orientation. The authors show how to design and steer the filters and present examples of their use in the analysis of orientation and phase, angularly adaptive filtering, edge detection, and shape from shading. One can also build a self-similar steerable pyramid representation. The same concepts can be generalized to the design of 3-D steerable filters. >
3,365 citations
"General Road Detection From a Singl..." refers background in this paper
...The kernels of the Gabor filters are similar to the 2D receptive field profiles of the mammalian cortical simple cells and exhibit desirable characteristics of spatial locality and orientation selectivity....
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TL;DR: The conditions under which a set of continuous 2D Gabor wavelets will provide a complete representation of any image are derived, and self-similar wavelet parametrization is found which allow stable reconstruction by summation as though the wavelets formed an orthonormal basis.
Abstract: This paper extends to two dimensions the frame criterion developed by Daubechies for one-dimensional wavelets, and it computes the frame bounds for the particular case of 2D Gabor wavelets. Completeness criteria for 2D Gabor image representations are important because of their increasing role in many computer vision applications and also in modeling biological vision, since recent neurophysiological evidence from the visual cortex of mammalian brains suggests that the filter response profiles of the main class of linearly-responding cortical neurons (called simple cells) are best modeled as a family of self-similar 2D Gabor wavelets. We therefore derive the conditions under which a set of continuous 2D Gabor wavelets will provide a complete representation of any image, and we also find self-similar wavelet parametrization which allow stable reconstruction by summation as though the wavelets formed an orthonormal basis. Approximating a "tight frame" generates redundancy which allows low-resolution neural responses to represent high-resolution images.
1,727 citations
"General Road Detection From a Singl..." refers background or methods in this paper
...The confidence in the orientation h b is given by Conf ebA f T 6 Average b % M-M-M o b ' o ebA We normalize Conf throughout the image to the range of 0 to 1....
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...In our experiments, we discard the pixels having a confidence score smaller than , and consider the remaining pixels as the “voting” pixels. can be seen as a threshold put on the normalized confidence score....
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TL;DR: The generic obstacle and lane detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety, allows to detect both generic obstacles and the lane position in a structured environment at a rate of 10 Hz.
Abstract: This paper describes the generic obstacle and lane detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an on-board monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.
1,088 citations
"General Road Detection From a Singl..." refers background in this paper
...Let \ ] S ' be the gray level value of an image at ] ....
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TL;DR: A comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented.
Abstract: Driver-assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively considered. It is therefore important to take a critical look at key aspects of these systems, one of which is lane-position tracking. It is for these driver-assistance objectives that motivate the development of the novel "video-based lane estimation and tracking" (VioLET) system. The system is designed using steerable filters for robust and accurate lane-marking detection. Steerable filters provide an efficient method for detecting circular-reflector markings, solid-line markings, and segmented-line markings under varying lighting and road conditions. They help in providing robustness to complex shadowing, lighting changes from overpasses and tunnels, and road-surface variations. They are efficient for lane-marking extraction because by computing only three separable convolutions, we can extract a wide variety of lane markings. Curvature detection is made more robust by incorporating both visual cues (lane markings and lane texture) and vehicle-state information. The experiment design and evaluation of the VioLET system is shown using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. A justification for the choice of metrics based on a previous study with human-factors applications as well as extensive ground-truth testing from different times of day, road conditions, weather, and driving scenarios is also presented. In order to design the VioLET system, an up-to-date and comprehensive analysis of the current state of the art in lane-detection research was first performed. In doing so, a comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented
1,056 citations
"General Road Detection From a Singl..." refers background in this paper
...I. INTRODUCTION NUMEROUS image-based road detection algorithms haveemerged as one of the components of fully automatic vehicle navigation systems [1]....
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TL;DR: A robust algorithm, called CHEVP, is presented for providing a good initial position for the B-Snake model, and a minimum error method by Minimum Mean Square Error (MMSE) is proposed to determine the control points of the B -Snake model by the overall image forces on two sides of lane.
812 citations
"General Road Detection From a Singl..." refers background in this paper
...For an orientation and a scale (radial frequency) , the Gabor wavelets (kernels,filters) are defined by [25] !...
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