Multiband Image Segmentation and Object Recognition for Understanding Road Scenes

Kang, Yousun; Yamaguchi, Kazuo; Naito, Takashi; Ninomiya, Yoshiki

doi:10.1109/tits.2011.2160539

Cited by 49 publications

(21 citation statements)

References 39 publications

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“…1a) usually perform pixel-level evaluation in the perspective space. Metrics include the classical true positive (TP) and false positive (FP) rates on the pixel/patch level [20], [21], [22], the accuracy [6] as well as precision/recall and the derived F-measure [7], [10], [18]. In order to capture also traffic participants, Alvarez et al [18] propose to incorporate vehicle detections into the evaluation measure.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to [7], [10], [18], we employ the F-measure derived from the precision and recall values (Eq. 1-3) for the pixel-based evaluation.…”

Section: A Classical Pixel-based Metricsmentioning

confidence: 99%

“…However, this requires the road area to be limited by sufficiently elevated structures as well as accurate depth information which for stereo cameras is only available in the close range. Consequently, many approaches put higher emphasis on appearance cues such as the color and texture of the road area [5], [6], [7], [8], [9], [10], [11], [12]. These visual properties of the road area have been used for estimating the overall road shape [12] or for segmenting the complete road area [5], [6], [8], [10].…”

Section: Introductionmentioning

confidence: 99%

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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)

566

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%

“…Similar to [7], [10], [18], we employ the F-measure derived from the precision and recall values (Eq. 1-3) for the pixel-based evaluation.…”

Section: A Classical Pixel-based Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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)

566

317

View full text Add to dashboard Cite

show abstract

“…However, classifying the visual appearance on a local scale only can lead to many ambiguities. Therefore, in [12], it was shown that incorporating a pixel's larger visual context by using multiscale grid histograms increases the detection quality of all classes.…”

Section: B Segmentation Of Road Areamentioning

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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“…To extract features, we compute the response of a Gabor filter oriented at the dominant direction of the neighborhood of a pixel, which is the same as method in [19]. We extract the color feature in the International Commission on Illumination (CIE) L*a*b* space because this color space can independently control color and intensity information.…”

Section: Color Feature Extraction With Gabor Filtermentioning

confidence: 99%

A Hierarchical Segmentation Approach towards Roads and Slopes for Collapse Recognition

Jia

Zhu

2013

IJSIP

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Multiband Image Segmentation and Object Recognition for Understanding Road Scenes

Cited by 49 publications

References 39 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

A Hierarchical Segmentation Approach towards Roads and Slopes for Collapse Recognition

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