12,403 research outputs found
Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera
Understanding ego-motion and surrounding vehicle state is essential to enable
automated driving and advanced driving assistance technologies. Typical
approaches to solve this problem use fusion of multiple sensors such as LiDAR,
camera, and radar to recognize surrounding vehicle state, including position,
velocity, and orientation. Such sensing modalities are overly complex and
costly for production of personal use vehicles. In this paper, we propose a
novel machine learning method to estimate ego-motion and surrounding vehicle
state using a single monocular camera. Our approach is based on a combination
of three deep neural networks to estimate the 3D vehicle bounding box, depth,
and optical flow from a sequence of images. The main contribution of this paper
is a new framework and algorithm that integrates these three networks in order
to estimate the ego-motion and surrounding vehicle state. To realize more
accurate 3D position estimation, we address ground plane correction in
real-time. The efficacy of the proposed method is demonstrated through
experimental evaluations that compare our results to ground truth data
available from other sensors including Can-Bus and LiDAR
A Joint 3D-2D based Method for Free Space Detection on Roads
In this paper, we address the problem of road segmentation and free space
detection in the context of autonomous driving. Traditional methods either use
3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or
stereo cameras or 2-dimensional (2D) cues such as lane markings, road
boundaries and object detection. Typical 3D point clouds do not have enough
resolution to detect fine differences in heights such as between road and
pavement. Image based 2D cues fail when encountering uneven road textures such
as due to shadows, potholes, lane markings or road restoration. We propose a
novel free road space detection technique combining both 2D and 3D cues. In
particular, we use CNN based road segmentation from 2D images and plane/box
fitting on sparse depth data obtained from SLAM as priors to formulate an
energy minimization using conditional random field (CRF), for road pixels
classification. While the CNN learns the road texture and is unaffected by
depth boundaries, the 3D information helps in overcoming texture based
classification failures. Finally, we use the obtained road segmentation with
the 3D depth data from monocular SLAM to detect the free space for the
navigation purposes. Our experiments on KITTI odometry dataset, Camvid dataset,
as well as videos captured by us, validate the superiority of the proposed
approach over the state of the art.Comment: Accepted for publication at IEEE WACV 201
Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System
Az http://intechweb.org/ alatti "Books" fĂŒl alatt kell rĂĄkeresni a "Stereo Vision" cĂmre Ă©s az 1. fejezetre
Homography-based ground plane detection using a single on-board camera
This study presents a robust method for ground plane detection in vision-based systems with a non-stationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments
Detecting shadows and low-lying objects in indoor and outdoor scenes using homographies
Many computer vision applications apply background suppression techniques for the detection and segmentation of moving objects in a scene. While these algorithms tend to work well in controlled conditions they often fail when applied to unconstrained real-world environments. This paper describes a system that detects and removes erroneously segmented foreground regions that are close to a ground plane. These regions include shadows, changing background objects and other low-lying objects such as leaves and rubbish. The system uses a set-up of two or more cameras and requires no 3D reconstruction or depth analysis of the regions. Therefore, a strong camera calibration of the set-up is not necessary. A geometric constraint called a homography is exploited to determine if foreground points are on or above the ground plane. The system takes advantage of the fact that regions in images off the homography plane will not correspond after a homography transformation. Experimental results using real world scenes from a pedestrian tracking application illustrate the effectiveness of the proposed approach
Multi-View 3D Object Detection Network for Autonomous Driving
This paper aims at high-accuracy 3D object detection in autonomous driving
scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework
that takes both LIDAR point cloud and RGB images as input and predicts oriented
3D bounding boxes. We encode the sparse 3D point cloud with a compact
multi-view representation. The network is composed of two subnetworks: one for
3D object proposal generation and another for multi-view feature fusion. The
proposal network generates 3D candidate boxes efficiently from the bird's eye
view representation of 3D point cloud. We design a deep fusion scheme to
combine region-wise features from multiple views and enable interactions
between intermediate layers of different paths. Experiments on the challenging
KITTI benchmark show that our approach outperforms the state-of-the-art by
around 25% and 30% AP on the tasks of 3D localization and 3D detection. In
addition, for 2D detection, our approach obtains 10.3% higher AP than the
state-of-the-art on the hard data among the LIDAR-based methods.Comment: To appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
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