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

Arróspide, Jon; Salgado, Luís; Nieto, Marcos; Mohedano, Raúl

doi:10.1049/iet-its.2009.0073

Cited by 47 publications

(30 citation statements)

References 15 publications

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“…Otherwise, the incoming measurement is not relied on and the system uses the predicted homography for further processing stages. The threshold for homography acceptance, t a , must be predefined according to kinetic constraints of the vehicles within their environment (see [12]) for an exhaustive discussion on the choice of t a ).…”

Section: B Homography Estimationmentioning

confidence: 99%

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

Arróspide

Salgado

Nieto

2010

2010 IEEE Intelligent Vehicles Symposium

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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.

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Section: B Homography Estimationmentioning

confidence: 99%

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

Arróspide

Salgado

Nieto

2010

2010 IEEE Intelligent Vehicles Symposium

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“…In the hypothesis generation stage, a quick search is performed throughout the image so that only a small subset of regions likely containing vehicles are retained. The search is typically based on some expected feature of vehicles, such as color [1], shadow [2], vertical edges [1], [3], or motion [4].…”

Section: Introductionmentioning

confidence: 99%

HOG-like gradient-based descriptor for visual vehicle detection

Arróspide

Salgado

Marinas

2012

2012 IEEE Intelligent Vehicles Symposium

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Abstract-One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%.

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“…As shown in Fig.5, after obtaining the first two keyframes, map initialization is done with a five-point algorithm [17] and RANSAC [31]. First, the essential matrix E between the two keyframes is calculated from a sample of 5 point correspondences:…”

Section: Methods Overviewmentioning

confidence: 99%

“…Another interesting method was raised by Arrospide et al in [17]. They found a vehicle via ground plane detection.…”

Section: Introductionmentioning

confidence: 99%

Combining Front Vehicle Detection with 3D Pose Estimation for a Better Driver Assistance

Peng

et al. 2012

International Journal of Advanced Robotic Systems

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Driver assistant systems enhance traffic safety and efficiency. The accurate 3D pose of a front vehicle can help a driver to make the right decision on the road. We propose a novel real-time system to estimate the 3D pose of the front vehicle. This system consists of two parallel threads: vehicle rear tracking and mapping. The vehicle rear is first identified in the video captured by an onboard camera, after license plate localization and foreground extraction. The 3D pose estimation technique is then employed with respect to the extracted vehicle rear. Most current 3D pose estimation techniques need prior models or a stereo initialization with user cooperation. It is extremely difficult to obtain prior models due to the varying appearance of vehicles' rears. Moreover, it is unsafe to ask for drivers' cooperation when a vehicle is running. In our system, two initial keyframes for stereo algorithms are automatically extracted by vehicle rear detection and tracking. Map points are defined as a collection of point features extracted from the vehicle's rear with their 3D information. These map points are inferences that relate the 2D features detected in following vehicles' rears with the 3D world. The relative 3D pose of the onboard camera to the front vehicle rear is then estimated through matching the map points with point features detected on the front vehicle rear. We demonstrate the capabilities of our system by testing on real-time and synthesized videos. In order to make the experimental analysis visible, we demonstrated an estimated 3D pose through augmented reality, which needs accurate and real-time 3D pose estimation.Keywords 3D pose estimation, vehicle rear tracking, driver assistance system Brief Abstract for Correspondences Driver assistant systems enhance traffic safety and efficiency. The accurate 3D pose of a front vehicle can help a driver to make the right decision on the road. We originally propose a real-time system to estimate the 3D pose of the front vehicle. This system consists of two parallel threads: vehicle rear tracking and mapping. The vehicle rear is first identified in the video captured by an onboard camera, after license plate localization and foreground extraction. The 3D pose estimation technique is then employed with respect to the extracted vehicle rear. We demonstrate the capabilities of our system by testing on real-time and synthesized videos. In order to make the experimental analysis visible, we demonstrated an estimated 3D pose through augmented reality, which needs accurate and real-time 3D pose estimation.

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

Cited by 47 publications

References 15 publications

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

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

HOG-like gradient-based descriptor for visual vehicle detection

Combining Front Vehicle Detection with 3D Pose Estimation for a Better Driver Assistance

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