Stabilization of Inverse Perspective Mapping Images based on Robust Vanishing Point Estimation

IET Intell. Transp. Syst.

et al. 2010

This study presents a robust method for ground plane detection in vision-based systems with a nonstationary 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.

Section: Ground Plane Estimationmentioning

confidence: 99%

Homography-based ground plane detection using a single on-board camera

Arróspide

IET Intell. Transp. Syst.

et al. 2010

“…In particular, we propose to restrict the search of feature points to those regions with highest probability to contain lane markings. The presented lane marking detector is an enhancement of that presented in [8], and is specially designed to provide reliable results for the posterior feature matching. Notwithstanding, note that any other feature extraction and matching approach may well be used within the system proposed for vehicle detection and tracking (see [9] [10] for other references on road feature extractors).…”

Section: A Feature Extraction and Matchingmentioning

confidence: 99%

Vehicle detection and tracking using homography-based plane rectification and particle filtering

Arróspide

2010 IEEE Intelligent Vehicles Symposium

2010

Self Cite

Abstract-This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results.I. INTRODUCTION Nowadays advanced driver assistance systems receive increasing interest both commercially and from the scientific community. In particular, much work has been devoted to the research on techniques for the detection of vehicles in traffic scenarios based on monocular computer vision, due to its low cost and good performance. Aside from traditional knowledge or feature-based approaches, which are dependent on the specific conditions (e.g., weather, illumination), use of motion information is in the basis of many recent works in the field, as this is inherent to the vehicles regardless of the conditions.Usually movement on a planar surface (i.e., the road) is assumed. Nonetheless, environments using non-stationary camera settings, such as the considered traffic environment, feature motion both of the own vehicle (ego-motion) and of the surrounding vehicles. Many methods have been presented that aim at computing ego-motion in order to decouple these motions [1][2]. However, reliable and efficient ego motion estimation in still and open issue.As for vehicle tracking, statistical approaches are adopted in many recent works. In particular, particle filters have emerged as a very powerful tool to perform tracking in a wide variety of applications [3] [4]. Many of these works assume a given initial detection and perform tracking using likelihood models based on appearance templates. However such detection-by-tracking approaches carry the danger of drifting away from the correct targets [5]. Additionally, tracking is often limited to a constant number of objects.

“…Our approach is fully adaptive to the unknown scenario conditions, without using prior information about the pose of the camera with respect to the road, which as opposed to most approaches in the literature is automatically retrieved through an adaptive computation of the image-plane to roadplane homography [2,20]. This transform, which is stabilized using a dynamic vanishing point estimation method, removes the inherent perspective distortion from the images and thus simplifies further analysis stages.…”

Section: Introductionmentioning

confidence: 99%

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Laborda

Machine Vision and Applications

2010

Self Cite

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance-cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classifier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.