Evaluation of Road Marking Feature Extraction

Veit, Thomas; Tarel, Jean-Philippe; Nicolle, Philippe; Charbonnier, Pierre

doi:10.1109/itsc.2008.4732564

Cited by 155 publications

(82 citation statements)

References 15 publications

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“…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). In particular, the homography calculation module in Section II-B works regardless of the used feature extraction method as long as a set of road plane correspondences is provided at its input.…”

Section: A Feature Extraction and Matchingmentioning

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: A Feature Extraction and Matchingmentioning

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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“…The most common metrics used for evaluating performance of lane detection algorithms are Precision, Recall, F-score, Accuracy [34], Receiver Operating Characteristic (ROC) curves and Dice Similarity Coefficient (DSC) [33]. Precision is the fraction of detected lanes markers that are actual lane markers.…”

Section: Performance Metrics Used For Lane Detection and Trackingmentioning

confidence: 99%

Review of Lane Detection and Tracking Algorithms in Advanced Driver Assistance System

Kumar¹,

Simon²

2015

IJCSIT

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“…In Fig. 1 is given an example with a road image from ROMA database [20]. The aim of this comparative example is to illustrate the colour geometry sketch and the colour corner extraction, and in particular to show how the effect associated to particular reference colour can be imposed.…”

Section: Application To Invariant Feature Detectionmentioning

confidence: 99%

Structure Tensor of Colour Quaternion Image Representations for Invariant Feature Extraction

Angulo

2009

Lecture Notes in Computer Science

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Abstract. Colour image representation using real quaternions has shown to be very useful for linear and morphological colour filtering. This paper deals with the extension of first derivatives-based structure tensor for various quaternionic colour image representations. Classical corner and edge features are obtained from eigenvalues of the quaternionic colour structure tensors. We study the properties of invariance of the quaternion colour spatial derivatives and their robustness for feature extraction on practical examples.

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Evaluation of Road Marking Feature Extraction

Cited by 155 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

Review of Lane Detection and Tracking Algorithms in Advanced Driver Assistance System

Structure Tensor of Colour Quaternion Image Representations for Invariant Feature Extraction

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