Stereovision-based 3D obstacle detection for automotive safety driving assistance

Ventroux, Nicolas; Schmit, Renaud; Pasquet, Frederic; Viel, Pierre-Emmanuel; Guyetant, Stéphane

doi:10.1109/itsc.2009.5309832

Cited by 11 publications

(12 citation statements)

References 6 publications

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“…This problem impedes applicability of classic algorithms in applications in which the processing time is crucial, e.g. driving assistive devices and motion analysis [9]. …”

Section: Classic Stereo Vision Problemmentioning

confidence: 99%

Asynchronous Event-based Cooperative Stereo Matching Using Neuromorphic Silicon Retinas

Firouzi

Conradt

2015

Neural Process Lett

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Biologically-inspired event-driven silicon retinas, so called dynamic vision sensors (DVS), allow efficient solutions for various visual perception tasks, e.g. surveillance, tracking, or motion detection. Similar to retinal photoreceptors, any perceived light intensity change in the DVS generates an event at the corresponding pixel. The DVS thereby emits a stream of spatiotemporal events to encode visually perceived objects that in contrast to conventional frame-based cameras, is largely free of redundant background information. The DVS offers multiple additional advantages, but requires the development of radically new asynchronous, event-based information processing algorithms. In this paper we present a fully event-based disparity matching algorithm for reliable 3D depth perception using a dynamic cooperative neural network. The interaction between cooperative cells applies cross-disparity uniqueness-constraints and within-disparity continuity-constraints, to asynchronously extract disparity for each new event, without any need of buffering individual events. We have investigated the algorithm's performance in several experiments; our results demonstrate smooth disparity maps computed in a purely event-based manner, even in the scenes with temporally-overlapping stimuli.

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“…This problem impedes applicability of classic algorithms in applications in which the processing time is crucial, e.g. driving assistive devices and motion analysis [9]. …”

Section: Classic Stereo Vision Problemmentioning

confidence: 99%

Asynchronous Event-based Cooperative Stereo Matching Using Neuromorphic Silicon Retinas

Firouzi

Conradt

2015

Neural Process Lett

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“…Such a discretized continuous pixel readout -reading the pixel value at pre-computed exposure time, and accumulating these different partial exposure -allows to get an optimally exposed frame. Typical acquisition speed for real time applications such as object tracking or obstacle detection has an typical frame rate of 30 frames per second 30 . In the case of an M sub-frames image subdivision, a 30 × M fps frame rate sensor is required.…”

Section: Constraints Imposed By the Design Of Feedbackmentioning

confidence: 99%

A new approach of smart vision sensors

et al. 2012

Self Cite

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Today's digital image sensors are used as passive photon integrators and image processing is essentially performed by digital processors separated from the image sensing part. This approach imposes to the processing part to deal with already grabbed pictures with possible unadjusted exposition parameters. This paper presents a fast self-adaptable preprocessing architecture with fast feedbacks on the sensing level. These feedbacks are controlled by digital processing in order to modify the sensor parameters during exposure time. Exposition and processing parameters are tuned in real life to fit with applications requirement depending on scene parameters. Considering emerging integration technologies such as 3D stacking, this paper presents an innovative way of designing smart vision sensors, integrating feedback control and opening new approaches for machine vision architectures.

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“…The second one is passive stereoscopy, with two or more cameras allowing a triangulation from the images (Darouich, 2010). N. Ventroux and R. Schimit (Ventroux, 2009) defines a solution to achieve a 3D reconstruction device based on stereoscopic method for autonomous cars. Kolar (Kolar, 2007) (Kolar, 2009) defines a way to integrate the 3D reconstruction into an integrated vision sensor for an endoscopic video capsule.…”

Section: Algorithms Used For Diagnosis Helpingmentioning

confidence: 99%