Robust road marking extraction in urban environments using stereo images

Sebsadji, Yazid; Tarel, Jean-Philippe; Foucher, Philippe; Charbonnier, Pierre

doi:10.1109/ivs.2010.5548075

Cited by 20 publications

(9 citation statements)

References 15 publications

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“…Crosswalk detection [1] [2] gradually becomes an active research area in the decades and is applied extensively in environment sense of traffic monitoring, road detection in traffic sense, traffic surveillance monitoring, vehicle and pedestrian detection and so on. It is still a challenge that how we detect accurately crosswalks and locate them on the road now.…”

Section: Introductionmentioning

confidence: 99%

Crosswalk Detection Based on MSER and ERANSAC

Zhai

Cui

et al. 2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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Crosswalks detection and location in traffic scenes plays an important role in the Intelligent Transportation Management System (ITMS). In this paper, we propose a new novel and robust approach for the detection and location of crosswalks, which is based on Maximally Stable Extremal Regions (MSER) and extended Random Sample Consensus (ERANSAC). Specially, first, the method with temporal median of background extraction is applied to traffic monitoring videos so that the foreground has a less disturbance for crosswalks detection and location. Second, we use a method of MSER to extract crosswalk features in the image. Last, we invent the ERANSAC to pick out crosswalk features extracted by MSER to detect and locate crosswalk and indicate the position of crosswalk in the image. Compared to the existing detection methods, the proposed method based on MSER can efficiently extract crosswalk regions under various illumination conditions, which can avoid the selection of thresholds according to the current environment situation and greatly improve the system flexibility and robustness. The ERANSAC can determine the distance between candidate samples and get the principle components by the way of eliminating the non-crosswalk regions. Promising experimental results have demonstrated that the proposed method has a high detection rate at extremely false alarm and is robust to the different crosswalk angle and various illuminations.

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Section: Introductionmentioning

confidence: 99%

Crosswalk Detection Based on MSER and ERANSAC

Zhai

Cui

et al. 2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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“…With a set of learned road marker templates, a road marker recognition approach is developed in Wu and Ranganathan (2012). As reported in Sebsadji et al (2010), stereo images can also be used to improve on the extraction accuracy. The most representative visual navigation for autonomous vehicle is from the VisLab, University of Parma in Italy.…”

Section: Introductionmentioning

confidence: 99%

Map-Enhanced Visual Taxiway Extraction for Autonomous Taxiing of UAVs∗∗This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner.

Chen

2015

IFAC-PapersOnLine

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In this paper, a map-enhanced method is proposed for vision-based taxiway centreline extraction, which is a prerequisite of autonomous visual navigation systems for unmanned aerial vehicles. Comparing with other sensors, cameras are able to provide richer information. Consequently, vision based navigations have been intensively studied in the recent two decades and computer vision techniques are shown to be capable of dealing with various problems in applications. However, there are significant drawbacks associated with these computer vision techniques that the accuracy and robustness may not meet the required standard in some application scenarios. In this paper, a taxiway map is incorporated into the analysis as prior knowledge to improve on the vehicle localisation and vision based centreline extraction. We develop a map updating algorithm so that the traditional map is able to adapt to the dynamic environment via Bayesian learning. The developed method is illustrated using a simulation study.

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“…As mentioned in [15], [16], [17], [18], the key issue is to extract the lane markings, which is crucial to the lane detection problem. In this paper, the focus is on road lane marking extraction by using a camera, gyroscope and GPS.…”

Section: Introductionmentioning

confidence: 99%

A lane marking extraction approach based on Random Finite Set Statistics

Zhang

Stähle

Chen

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-Within the past few years, lane detection technology has become of high interest in the field of intelligent vehicles; however, robustness is still an issue. The challenge is to extract the lane markings effectively from the complex urban environment. In this paper, we present a novel approach based on Random Finite Set Statistics for estimating the position of lane markings. We rely on Probability Hypothesis Density (PHD) filtering and apply this technique to lane marking extraction in urban environment. Our method is based on two phases: an image preprocessing phase to extract pixels that potentially represent lanes and a tracking phase to identify lane markings. Compared to other approaches, our method presents a recursive filtering algorithm which extracts lane markings in the presence of clutter and non-lane markings.The experimental results exhibit the high performance of the proposed approach under various scenarios.

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Robust road marking extraction in urban environments using stereo images

Cited by 20 publications

References 15 publications

Crosswalk Detection Based on MSER and ERANSAC

Crosswalk Detection Based on MSER and ERANSAC

A lane marking extraction approach based on Random Finite Set Statistics

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