On feature templates for Particle Filter based lane detection

Linarth, A.; Angelopoulou, Elli

doi:10.1109/itsc.2011.6083016

Cited by 16 publications

(10 citation statements)

References 17 publications

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“…1b, are traditionally evaluated via the distance of the estimated lane marking to the ground truth borders in the image. By allowing a flexible margin for counting successful border candidates, TP and FP rates can be obtained [24], [25], [26], [27]. The metric deviations of the borders of the road using a segmentation approach in the BEV space are evaluated in [8].…”

Section: Related Workmentioning

confidence: 99%

A new performance measure and evaluation benchmark for road detection algorithms

Fritsch

Kühnl

Geiger

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

560

317

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Abstract-Detecting the road area and ego-lane ahead of a vehicle is central to modern driver assistance systems. While lane-detection on well-marked roads is already available in modern vehicles, finding the boundaries of unmarked or weakly marked roads and lanes as they appear in inner-city and rural environments remains an unsolved problem due to the high variability in scene layout and illumination conditions, amongst others. While recent years have witnessed great interest in this subject, to date no commonly agreed upon benchmark exists, rendering a fair comparison amongst methods difficult.In this paper, we introduce a novel open-access dataset and benchmark for road area and ego-lane detection. Our dataset comprises 600 annotated training and test images of high variability from the KITTI autonomous driving project, capturing a broad spectrum of urban road scenes. For evaluation, we propose to use the 2D Bird's Eye View (BEV) space as vehicle control usually happens in this 2D world, requiring detection results to be represented in this very same space. Furthermore, we propose a novel, behavior-based metric which judges the utility of the extracted ego-lane area for driver assistance applications by fitting a driving corridor to the road detection results in the BEV. We believe this to be important for a meaningful evaluation as pixel-level performance is of limited value for vehicle control. State-of-the-art road detection algorithms are used to demonstrate results using classical pixellevel metrics in perspective and BEV space as well as the novel behavior-based performance measure. All data and annotations are made publicly available on the KITTI online evaluation website in order to serve as a common benchmark for road terrain detection algorithms.

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Section: Related Workmentioning

confidence: 99%

A new performance measure and evaluation benchmark for road detection algorithms

Fritsch

Kühnl

Geiger

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

560

317

View full text Add to dashboard Cite

show abstract

“…Today's state-of-the-art approaches extract the delimiting elements of the driving space either for the detection of the ego-lane (see, e.g., [4]- [7], [15], and [16]) or for the detection of the complete road area (see, e.g., [17]- [19]). Features for these models are extracted from longitudinal road structures such as lane markings or road boundary obstacles (e.g., curbstones and barriers) by visual processing.…”

Section: A Extraction Of Road Delimitersmentioning

confidence: 99%

“….) detectable due to, e.g., parking cars on the side occluding them, then current systems based on delimiter detection [4]- [7] are not working. Furthermore, also the appearance of the road itself, i.e., the color and texture of the asphalt, is strongly varying and makes appearance-based road segmentation [8]- [10] highly challenging.…”

Section: Introductionmentioning

confidence: 99%

Monocular Road Terrain Detection by Combining Visual and Spatial Information

Fritsch

Kühnl

Kummert

2014

IEEE Trans. Intell. Transport. Syst.

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For future driver assistance systems and autonomous vehicles, the road course, i.e., the width and shape of the driving path, is an important source of information. In this paper, we introduce a new hierarchical two-stage approach for learning the spatial layout of road scenes. In the first stage, base classifiers analyze the local visual properties of patches extracted from monocular camera images and provide metric confidence maps. We use classifiers for road appearance, boundary appearance, and lane-marking appearance. The core of the proposed approach is the computation of SPatial RAY (SPRAY) features from each metric confidence map in the second stage. A boosting classifier selecting discriminative SPRAY features can be trained for different types of road terrain and allows capturing the local visual properties together with their spatial layout in the scene. In this paper, the extraction of road area and ego-lane on inner-city video streams is demonstrated. In particular, the detection of the ego-lane is a challenging semantic segmentation task showing the power of SPRAY features, because on a local appearance level, the ego-lane is not distinguishable from other lanes. We have evaluated our approach operating at 20 Hz on a graphics processing unit on a publicly available data set, demonstrating the performance on a variety of road types and weather conditions.

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“…Numerous line detection algorithms and techniques have recently been proposed [7][8][9][10][11][12]. Among these algorithms, the Hough transform is one of the most robust and extensively used [13][14][15][16][17].The Hough transform is implemented according to (1):…”

Section: Line Detection Algorithmsmentioning

confidence: 99%

Real-time lane departure warning system based on a single FPGA

Shang

Song

et al. 2013

J Image Video Proc

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This paper presents a camera-based lane departure warning system implemented on a field programmable gate array (FPGA) device. The system is used as a driver assistance system, which effectively prevents accidents given that it is endowed with the advantages of FPGA technology, including high performance for digital image processing applications, compactness, and low cost. The main contributions of this work are threefold. (1) An improved vanishing point-based steerable filter is introduced and implemented on an FPGA device. Using the vanishing point to guide the orientation at each pixel, this algorithm works well in complex environments. (2) An improved vanishing point-based parallel Hough transform is proposed. Unlike the traditional Hough transform, our improved version moves the coordinate origin to the estimated vanishing point to reduce storage requirements and enhance detection capability. (3) A prototype based on the FPGA is developed. With improvements in the vanishing point-based steerable filter and vanishing point-based parallel Hough transform, the prototype can be used in complex weather and lighting conditions. Experiments conducted on an evaluation platform and on actual roads illustrate the effective performance of the proposed system.

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On feature templates for Particle Filter based lane detection

Cited by 16 publications

References 17 publications

A new performance measure and evaluation benchmark for road detection algorithms

A new performance measure and evaluation benchmark for road detection algorithms

Monocular Road Terrain Detection by Combining Visual and Spatial Information

Real-time lane departure warning system based on a single FPGA

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