A Stereovision Method for Obstacle Detection and Tracking in Non-Flat Urban Environments

Yu, Qian; Araújo, Hélder; Wang, Hong

doi:10.1007/s10514-005-0612-6

Cited by 29 publications

(13 citation statements)

References 18 publications

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“…Once detected, the equation of the ground plane can be recovered and used to compute the distance between a 3D point of the scene and the ground plane. The RANSAC plane fitting [12] is a commonly used method to fit a plane in the 3D space, but is rather computationally expensive. A recent method, called the V-disparity image [8,13], allows to detect the ground plane more easily with the depth image.…”

Section: Ground Plane Detectionmentioning

confidence: 99%

Fall Detection from Depth Map Video Sequences

Rougier

Auvinet

Rousseau

et al. 2011

Lecture Notes in Computer Science

136

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Abstract. Falls are one of the major risks for seniors living alone at home. Computer vision systems, which do not require to wear sensors, offer a new and promising solution for fall detection. In this work, an occlusion robust method is presented based on two features: human centroid height relative to the ground and body velocity. Indeed, the first feature is an efficient solution to detect falls as the vast majority of falls ends on the ground or near the ground. However, this method can fail if the end of the fall is completely occluded behind furniture. Fortunately, these cases can be managed by using the 3D person velocity computed just before the occlusion.

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Section: Ground Plane Detectionmentioning

confidence: 99%

Fall Detection from Depth Map Video Sequences

Rougier

Auvinet

Rousseau

et al. 2011

Lecture Notes in Computer Science

136

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“…Also, it detects only the moving obstacles and fails when obstacle has small or null speed (static ones). (2) The method based on stereo vision [10][11][12][13]. Images are captured using two or more cameras at the same time from different angles, and then obstacles are detected by matching.…”

Section: Introductionmentioning

confidence: 99%

A classification of on-road obstacles according to their relative velocities

Bendjaballah

Graovac

Boulahlib

2016

J Image Video Proc.

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The systems based on image processing have numerous applications in the domain of motion control of robots and autonomous vehicles. The current paper is oriented to the solution of the problem that precedes the implementation of automatic avoidance of the on-road obstacles-how to detect them, to track in the sequence of images, and to recognize which of them are stationary, incoming, or outgoing from the camera. The overall algorithm of obstacle classification presented in this paper consists of three basic phases: (1) image segmentation in order to extract the pixels belonging to the image of a road and the objects over it; (2) extraction of characteristic points inside the area of the obstacle, their description and tracking in following frames; and (3) estimation of distances between the camera, the obstacles and their rates of change (relative velocities). The verifications of particular steps of the proposed algorithm are illustrated using real road-traffic images, while the overall algorithm is tested using both synthesized sequences of images and the ones acquired in real driving.

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“…These systems provide the 3D perception of the environment which is employed in Advanced Driver Assistance Systems (ADAS) to support a variety of functions including obstacles detection [1], lane departure warning [2] and collision warning systems [3]. While the depth measurement precision of stereo vision systems is not as high as with active sensors such as RADAR and LIDAR, the stereo camera can compete with these active technologies due to its low cost in one hand and the amount of traffic scence information it provides in the other hand.…”

Section: Introductionmentioning

confidence: 99%

Vehicles Detection in Stereo Vision Based on Disparity Map Segmentation and Objects Classification

Dekkiche

Vincke

Merigot

2015

Advances in Visual Computing

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Abstract. This paper presents a coarse to fine approach of on-road vehicles detection and distance estimation based on the disparity map segmentation supervised by stereo vision. Scene segmentation is first performed relying on the robustness of the UV-disparity maps to generate free space and obstacles space. This last is investigated for on-road vehicles detection. The detection process starts with off-road objects substraction based on the connected component labeling algorithm which is also used for on-road segments extraction instead of the traditional hough transform for more robust, precise and fast detection. Objects classification is then applied to the on-road segments by using some cues describing the geometry of vehicles like width, height. However, these latter have been measured not in meter but rather in pixels in function of the disparity. The whole approach is presented and the experimental results of evaluation are shown.

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A Stereovision Method for Obstacle Detection and Tracking in Non-Flat Urban Environments

Cited by 29 publications

References 18 publications

Fall Detection from Depth Map Video Sequences

Fall Detection from Depth Map Video Sequences

A classification of on-road obstacles according to their relative velocities

Vehicles Detection in Stereo Vision Based on Disparity Map Segmentation and Objects Classification

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