Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird'seye-view images contain sharper features (e.g, road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford Robot-Car Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.
Several works have tried to adjust for inaccuracies caused by invalidity of the first two assumptions. The authors of @cite_34 @cite_41 used vanishing point detection, @cite_7 estimated the slope of the road according to the lane markings, and @cite_14 employed motion estimation obtained from SLAM. Invalidity of the third assumption is tackled in @cite_18 by using a laser scanner to exclude obstacles from being transformed to IPM. Another approach @cite_16 @cite_9 @cite_4 creates a look up table for all pixels, by taking into account the distance of objects on the road surface, in order to reduce artefacts at further distance. However, these methods generally assume simple environments (i.e. highway). Contrarily, we learn a non-linear mapping more suited for urban scenes.
@cite_18 @cite_14 @cite_4 @cite_7 @cite_41 @cite_9 @cite_16 @cite_34
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In this work, a new inverse perspective mapping (IPM) technique is proposed based on a robust estimation of the vanishing point, which provide bird-view images of the road, so that facilitating the tasks of road modeling and vehicle detection and tracking. This new approach has been design to cope with the instability that cameras mounted on a moving vehicle suffer. The estimation of the vanishing point relies on a novel and efficient feature extraction strategy, which segmentates the lane markings of the images by combining a histogram-based segmentation with temporal and frequency filtering. Then, the vanishing point of each image is stabilized by means of a temporal filtering along the estimates of previous images. In a last step, the IPM image is computed based on the stabilized vanishing point. Tests have been carried out on several long video sequences captured from cameras inside a vehicle being driven along highways and local roads, with different illumination and weather conditions, presence of shadows, occluding vehicles, and slope changes. Results have shown a significant improvement in terms of lane width constancy and parallelism between lane markings over non-stabilized IPM algorithms. In this paper, a technique for the identification of the unwanted lane departure of a traveling vehicle on a road is proposed. A piecewise linear stretching function (PLSF) is used to improve the contrast level of the region of interest (ROI). Lane markings on the road are detected by dividing the ROI into two subregions and applying the Hough transform in each subregion independently. This segmentation approach improves the computational time required for lane detection. For lane departure identification, a distance-based departure measure is computed at each frame, and a necessary warning message is issued to the driver when such measure exceeds a threshold. The novelty of the proposed algorithm is the identification of the lane departure only using three lane-related parameters based on the Euclidean distance transform to estimate the departure measure. The use of the Euclidean distance transform in combination with the PLSF keeps the false alarm around 3 and the lane detection rate above 97 under various lighting conditions. Experimental results indicate that the proposed system can detect lane boundaries in the presence of several image artifacts, such as lighting changes, poor lane markings, and occlusions by a vehicle, and it issues an accurate lane departure warning in a short time interval. The proposed technique shows the efficiency with some real video sequences. 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 on the detected vanishing point. The main technical contributions of the proposed approach are a novel adaptive soft voting scheme based on variable-sized voting region using confidence-weighted Gabor filters, which compute the dominant texture orientation at each pixel, 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 both computationally efficient and effective at detecting road regions in challenging conditions. In this paper, we propose a robust vanishing-point constrained lane detection method with a stereo-rig. This method can achieve promising detection performance for both straight and curved lanes without assuming any parametric lane model. First, we propose an accurate and efficient road vanishing point detection scheme based on the v-disparity and visual odometry techniques, where the v-disparity map can significantly reduce the searching space for vanishing point, and the visual odometry can benefit the vanishing point detection of both straight and curved roads. Next, we formulate the lane detection problem as a graph-search procedure, where a vanishing-point constrained Dijkstra shortest-path lane model is proposed to obtain a minimum-cost map. The two lane borders can be detected by finding two optimal paths which originate from the vanishing point to two cost-map derived terminal points, respectively. The proposed method has been tested on the KITTI and the Oxford RobotCar data sets and it works accurately and robustly on a variety of road scenes.