Road segmentation and obstacle detection by a fast watershed transformation

Beucher, Serge; Bilodeau, Michel

doi:10.1109/ivs.1994.639531

Cited by 54 publications

(22 citation statements)

References 5 publications

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“…Methods based on monocular vision are rare compared to edge based methods, e.g. there is a report on method using watershed transformation [7].…”

Section: Introductionmentioning

confidence: 99%

Road region segmentation based on sequential Monte-Carlo estimation

Procházka

2008

2008 10th International Conference on Control, Automation, Robotics and Vision

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Road region is an important information for guidance of autonomous vehicles or robots. The goal of this research is to develop robust monocular algorithm suitable for region based road detection. This paper deals with a problem, how to estimate probability density function (pdf) of road region appearing in sequential images. The key idea is to construct pdf from temporal sequence of observations throughout the image sequence, where pdf has color components and spatial coordinates as its variables. The problem of pdf estimation was formulated in terms of Bayesian filtering, and sequential Monte-Carlo method was addopted as a tool to solve the problem. The proposed method was evaluated on real image sequences, and effectiveness of the proposed method is demonstrated.

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“…Methods based on monocular vision are rare compared to edge based methods, e.g. there is a report on method using watershed transformation [7].…”

Section: Introductionmentioning

confidence: 99%

Road region segmentation based on sequential Monte-Carlo estimation

Procházka

2008

2008 10th International Conference on Control, Automation, Robotics and Vision

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“…Nevertheless, since the final target of obstacle detection is the determination of the free space in front of the vehicle and not the complete 3-D reconstruction of the world, camera calibration becomes less critical. For this reason, even if the movements of the vehicle modify some of the calibration parameters (camera height and inclination with respect of the road plane), a dynamic recalibration of the system is not required 3 . For comparison purposes, the ranging values for cameras height cm) and inclination larger than the ones estimated in [33] have been considered.…”

Section: Discussionmentioning

confidence: 99%

“…The techniques implemented in the previously mentioned systems range from the determination of the characteristics of painted lane markings [30] eventually aided by color information [19] to the use of deformable templates (such as LOIS [31], DBS [7], or ARCADE [29]), to an edge-based recognition using a morphological paradigm [3], [5], [59], to a model-based approach (as implemented in VaMoRs [26] or SCARF [17]). A model-based analysis of road markings has also been used to perform the analysis of intersections in city traffic images [21], [32]; nevertheless, as discussed in [46], the use of a model-based search approach has several drawbacks, such as the problem of using and maintaining an appropriate geometrical road model, the difficulty in detecting and matching complex road features, and the complexity of the computations involved.…”

Section: A Lane Detectionmentioning

confidence: 99%

GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection

Bertozzi

Broggi

1998

IEEE Trans. on Image Process.

893

404

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Abstract-This paper describes the Generic Obstacle and Lane Detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an onboard monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.

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“…Beucher and his colleagues [19,5] worked on road segmentation and obstacle detection based on watersheds. Their techniques consist of applying a temporal filter for noise reduction (and connection of ground markings), followed by edge detection and watershed segmentation.…”

Section: Related Workmentioning

confidence: 99%