Long Distance Vision Sensor for Driver Assistance

Duvieubourg, Luc; François, Clément; Ambellouis, Sébastien

doi:10.3182/20070903-3-fr-2921.00057

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…12 As a consequence, in the automotive field there is no reliable stereo-vision system to detect obstacles on the road surface at longer distances. In order to overcome this problem, in Duvieubourg et al 13 a long distance stereo-vision sensor for driver assistance providing images with sufficient resolution in order to discriminate between obstacles, lanes and objects at long-distances is proposed. However, only simulation results of detection up to 200 m have been presented.…”

Section: Related Workmentioning

confidence: 99%

SMART on-board multi-sensor obstacle detection system for improvement of rail transport safety

Ristić-Durrant¹,

Haseeb²,

Banić³

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part F:

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This paper presents an on-board multi-sensor system which is able to detect obstacles and estimate their distances in railway scenes in different illumination conditions. The system was developed within the H2020 Shift2Rail project SMART (Smart Automation of Rail Transport) and aims at increasing the safety of rail transport by detecting obstacles on the rail tracks ahead of a moving train in order to reduce the number of collisions. The system hardware consists of cameras of different types integrated into a specially designed housing, mounted on the front of the train. Multiple vision sensors complement each other in order to handle different illumination and environmental conditions. The system software uses a novel machine learning-based method that is suited to a particular challenge of railway operations, the need for long-range obstacle detection and distance estimation. The development of this method used a long-range railway dataset, which was specifically generated for this project. Evaluation results of reliable obstacle detection in various environmental conditions using the SMART RGB camera in day light illumination conditions and using the SMART Night Vision sensor in poor (night) illumination conditions are presented. The results demonstrate both the potential of the on-board SMART obstacle detection system in the operational railway environment and the benefit of the use of different cameras to be more independent of light and environmental conditions.

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Section: Related Workmentioning

confidence: 99%

SMART on-board multi-sensor obstacle detection system for improvement of rail transport safety

Ristić-Durrant¹,

Haseeb²,

Banić³

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part F:

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“…A schematic top view of the stereoscopic sensor is presented in figure 4(a). The stereoscopic sensor is composed of a single camera, two lateral plane mirrors, labeled as (a) and (b) We can find a description of the stereoscope and the characteristics of the active vision setup in [27], [28]. As this sensor has a small field of view, we can observe only a part of the scene in front of the vehicle.…”

Section: Sensor Descriptionmentioning

confidence: 99%

A visual attention focusing system using an active stereoscopic vision sensor

Ducrocq¹,

Bahrami²,

Duvieubourg

et al. 2010

2010 2nd International Conference on Image Processing Theory, Tools and Applications

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Humans have the capacity to move their eyes. Thanks to this capacity, they can orient their gaze to look at a relevant object inside a complex scene. In this paper, we have implemented a driver assistance application which tries to mimic this human capacity. We focus specially on highway driving situations, where the detection of obstacles must be done far away in front of the car. The implementation of the gaze control and orientation is obtained by an active vision system. We know that the human gaze is related to the visual attention which is a result of human perception and cognitive phenomenon. Several studies have shown that human perception and more specially the visual perception can be decomposed into a bottom-up process and a top-down process. Most of researches focused on the bottom-up process. In this work, in order to mimic the human behavior or at least improve vision systems, we use a new active stereovision setup and a model of the human visual perception based on the two previous approaches. Moreover, in a bottom-up approach, we add the depth information obtained with the stereoscopic sensor to the classical features used by other works. The topdown process is computed by the global knowledge of the scene and its features. Some results obtained by mean of a virtual road sequence, show the orientation of the field of view of the stereoscopic sensor toward relevant objects, given our criteria.

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“…For a specific pose of the lateral mirrors and for a rotation angle γ equal to zero, we have shown that the optical axis of a virtual camera is parallel to the optical axis of the real camera [15]. When the prism is in rotation, the optical axes of both virtual cameras remain parallel, but the baseline is slightly modified [16]. Figure 1(b) shows that for a rotation γ of the prism the orientation of the field of view is twice the angle γ, but in the opposite direction.…”

Section: Sensor Descriptionmentioning

confidence: 99%

“…The vergence and the baseline are adjusted manually by a fine tuning of mirrors locations and orientations. The optical axes of the cameras are parallel [16]. Finally, the baseline has a fixed value, computed with respect to our application to obtain a good compromise between compactedness and precision of the 3D map.…”

Section: The Gaze Control Platformmentioning

confidence: 99%

An Effective Active Vision System for Gaze Control

Ducrocq¹,

Bahrami²,

Duvieubourg³

et al. 2008

Advances in Visual Computing

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This paper presents the performances of an active vision system that mimic the human gaze control. A human can shift his gaze either by quickly moving his fixation point or by keeping a moving target in the fovea (high resolution). These two visual phenomena are called saccadic and smooth pursuit eye movements respectively. In order to mimic this human behavior, we have developed a novel active vision system based on a particular stereo-vision setup. It is composed with one camera, one prism and a set of mirrors. To point the field of view of the sensor at a target, the prism is rotated about its axis by a motorized stage. The system is designed for fast and accurate dynamical adjustments of gaze.To study the mechanical performances of our active vision system we have used three different but classical input signals. A step signal that simulates a change of target (saccadic eye movement), a velocity ramp and a sinusoidal signal that simulate a moving target (smooth pursuit). Whatever the input signal, the objective is to maintain the target in the middle of the image. The experiments demonstrate the efficiency of our vision sensor, in term of dynamical properties and measurement accuracy.

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Long Distance Vision Sensor for Driver Assistance

Cited by 4 publications

References 2 publications

SMART on-board multi-sensor obstacle detection system for improvement of rail transport safety

SMART on-board multi-sensor obstacle detection system for improvement of rail transport safety

A visual attention focusing system using an active stereoscopic vision sensor

An Effective Active Vision System for Gaze Control

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