Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera

• URL: http://arxiv.org/abs/2002.12492v1
• Date: Fri, 28 Feb 2020 00:24:18 GMT
• Title: Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera
• Authors: Stanislav Panev, Francisco Vicente, Fernando De la Torre and V\'eronique Prinet
• Abstract summary: We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens. Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
• Score: 74.45649274085447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a robust method for estimating road curb 3D parameters (size, location, orientation) using a calibrated monocular camera equipped with a fisheye lens. Automatic curb detection and localization is particularly important in the context of Advanced Driver Assistance System (ADAS), i.e. to prevent possible collision and damage of the vehicle's bumper during perpendicular and diagonal parking maneuvers. Combining 3D geometric reasoning with advanced vision-based detection methods, our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%, as well as its orientation, height and depth. Our approach consists of two distinct components - curb detection in each individual video frame and temporal analysis. The first part comprises of sophisticated curb edges extraction and parametrized 3D curb template fitting. Using a few assumptions regarding the real world geometry, we can thus retrieve the curb's height and its relative position w.r.t. the moving vehicle on which the camera is mounted. Support Vector Machine (SVM) classifier fed with Histograms of Oriented Gradients (HOG) is used for appearance-based filtering out outliers. In the second part, the detected curb regions are tracked in the temporal domain, so as to perform a second pass of false positives rejection. We have validated our approach on a newly collected database of 11 videos under different conditions. We have used point-wise LIDAR measurements and manual exhaustive labels as a ground truth.

Related papers

• Decoupling the Curve Modeling and Pavement Regression for Lane Detection [67.22629246312283]
  curve-based lane representation is a popular approach in many lane detection methods. We propose a new approach to the lane detection task by decomposing it into two parts: curve modeling and ground height regression.
  arXiv  Detail & Related papers  (2023-09-19T11:24:14Z)
• An Efficient Wide-Range Pseudo-3D Vehicle Detection Using A Single Camera [10.573423265001706]
  This paper proposes a novel wide-range Pseudo-3D Vehicle Detection method based on images from a single camera. To detect pseudo-3D objects, our model adopts specifically designed detection heads. Joint constraint loss combining both the object box and SPL is designed during model training, improving the efficiency, stability, and prediction accuracy of the model.
  arXiv  Detail & Related papers  (2023-09-15T12:50:09Z)
• AdaptiveShape: Solving Shape Variability for 3D Object Detection with Geometry Aware Anchor Distributions [1.3807918535446089]
  3D object detection with point clouds and images plays an important role in perception tasks such as autonomous driving. Current methods show great performance on detection and pose estimation of standard-shaped vehicles but lack behind on more complex shapes. This work introduces several new methods to improve and measure the performance for such classes.
  arXiv  Detail & Related papers  (2023-02-28T12:31:31Z)
• Satellite Image Based Cross-view Localization for Autonomous Vehicle [59.72040418584396]
  This paper shows that by using an off-the-shelf high-definition satellite image as a ready-to-use map, we are able to achieve cross-view vehicle localization up to a satisfactory accuracy. Our method is validated on KITTI and Ford Multi-AV Seasonal datasets as ground view and Google Maps as the satellite view.
  arXiv  Detail & Related papers  (2022-07-27T13:16:39Z)
• ONCE-3DLanes: Building Monocular 3D Lane Detection [41.46466150783367]
  We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. By exploiting the explicit relationship between point clouds and image pixels, a dataset annotation pipeline is designed to automatically generate high-quality 3D lane locations.
  arXiv  Detail & Related papers  (2022-04-30T16:35:25Z)
• Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving [74.74519047735916]
  3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors. Data collected for other use cases (such as virtual reality, gaming, and animation) may not be usable for AV applications. We propose one of the first approaches to alleviate this problem in the AV setting.
  arXiv  Detail & Related papers  (2021-12-22T18:57:16Z)
• Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data [19.63193201107591]
  7DoF prediction of vehicles at an intersection is an important task for assessing potential conflicts between road users. We develop an approach using a weakly supervised method of fine tuning 3D object detectors for traffic observation cameras. Our method achieves vehicle 7DoF pose prediction accuracy on our dataset comparable to the top performing monocular 3D object detectors on autonomous vehicle datasets.
  arXiv  Detail & Related papers  (2021-10-21T08:26:48Z)
• Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography [12.062095895630563]
  This paper proposes a method to extract the position and pose of vehicles in the 3D world from a single traffic camera. We observe that the homography between the road plane and the image plane is essential to 3D vehicle detection. We propose a new regression target called textittailedr-box and a textitdual-view network architecture which boosts the detection accuracy on warped BEV images.
  arXiv  Detail & Related papers  (2021-03-29T02:57:37Z)
• M3DSSD: Monocular 3D Single Stage Object Detector [82.25793227026443]
  We propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. The proposed M3DSSD achieves significantly better performance than the monocular 3D object detection methods on the KITTI dataset.
  arXiv  Detail & Related papers  (2021-03-24T13:09:11Z)
• PerMO: Perceiving More at Once from a Single Image for Autonomous Driving [76.35684439949094]
  We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image. Our approach combines the strengths of deep learning and the elegance of traditional techniques. We have integrated these algorithms with an autonomous driving system.
  arXiv  Detail & Related papers  (2020-07-16T05:02:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.