Adaptive multi-cue fusion for robust detection of unmarked inner-city streets

Michalke, Thomas; Kastner, Robert; Herbert, Michael; Fritsch, Jannik; Goerick, Christian

doi:10.1109/ivs.2009.5164243

Cited by 16 publications

(10 citation statements)

References 23 publications

(42 reference statements)

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“…For classifying the resulting features, a GentleBoost algorithm (100 iterations, four tree splits) is used. For evaluation, we apply the "quality" measure (6) (see also [39]) because it considers all errors, also including FP values. We measure both perspective image space quality Q P and metric space quality Q M .…”

Section: ) Individual Featuresmentioning

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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Section: ) Individual Featuresmentioning

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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“…By dynamic estimation of probability distributions over these features a pixelwise mask of the unmarked street is obtained. A more detailed description of the approach is presented in [17]. The resulting mask is used to approximate the 3D road surface by a plane.…”

Section: A Preprocessingmentioning

confidence: 99%

System approach for multi-purpose representations of traffic scene elements

Schmuedderich

Einecke

Hasler

et al. 2010

13th International IEEE Conference on Intelligent Transportation Systems

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Abstract-A major step towards intelligent vehicles lies in the acquisition of an environmental representation of sufficient generality to serve as the basis for a multitude of different assistance-relevant tasks. This acquisition process must reliably cope with the variety of environmental changes inherent to traffic environments. As a step towards this goal, we present our most recent integrated system performing object detection in challenging environments (e.g., inner-city or heavy rain). The system integrates unspecific and vehicle-specific methods for the detection of traffic scene elements, thus creating multiple object hypotheses. Each detection method is modulated by optimized models of typical scene context features which are used to enhance and suppress hypotheses. A multi-object tracking and fusion process is applied to make the produced hypotheses spatially and temporally coherent. In extensive evaluations we show that the presented system successfully analyzes scene elements under diverse conditions, including challenging weather and changing scenarios. We demonstrate that the used generic hypothesis representations allow successful application to a variety of tasks including object detection, movement estimation, and risk assessment by time-to-contact evaluation.

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“…Common approaches to road detection use pixel-level features (such as color [2], [5]- [8] or texture [9], [10]) to characterize the appearance of the road and group pixels in two different groups, namely, drivable road and background. The performance of these algorithms is commonly improved considering temporal information based on heuristic rules [5] or temporal smoothing [11], [12]. For instance, in [11], Michalke et al averaged past detection results to constraint the analysis of the current image.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of these algorithms is commonly improved considering temporal information based on heuristic rules [5] or temporal smoothing [11], [12]. For instance, in [11], Michalke et al averaged past detection results to constraint the analysis of the current image. In [12], Alvarez et al used time series analysis to predict the expected results instead of simple averages over past results.…”

Section: Introductionmentioning

confidence: 99%

Road Geometry Classification by Adaptive Shape Models

Álvarez

Gevers

Diego

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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Abstract-Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions.

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Adaptive multi-cue fusion for robust detection of unmarked inner-city streets

Cited by 16 publications

References 23 publications

Monocular Road Terrain Detection by Combining Visual and Spatial Information

Monocular Road Terrain Detection by Combining Visual and Spatial Information

System approach for multi-purpose representations of traffic scene elements

Road Geometry Classification by Adaptive Shape Models

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