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WO2010139351A1 - Systeme et procede de vision pour vehicule a moteur - Google Patents

Systeme et procede de vision pour vehicule a moteur Download PDF

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Publication number
WO2010139351A1
WO2010139351A1 PCT/EP2009/004075 EP2009004075W WO2010139351A1 WO 2010139351 A1 WO2010139351 A1 WO 2010139351A1 EP 2009004075 W EP2009004075 W EP 2009004075W WO 2010139351 A1 WO2010139351 A1 WO 2010139351A1
Authority
WO
WIPO (PCT)
Prior art keywords
noise level
image data
safety system
level calculation
tracking
Prior art date
Application number
PCT/EP2009/004075
Other languages
English (en)
Inventor
Anders Bergmark
Original Assignee
Autoliv Development Ab
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Autoliv Development Ab filed Critical Autoliv Development Ab
Priority to PCT/EP2009/004075 priority Critical patent/WO2010139351A1/fr
Publication of WO2010139351A1 publication Critical patent/WO2010139351A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/277—Analysis of motion involving stochastic approaches, e.g. using Kalman filters

Definitions

  • the invention relates to a vision system for a motor vehicle, comprising an imaging means for recording images of a region in front of the motor vehicle, object identifying means for processing image data provided by the imaging means in order to identify objects in the imaged scene, and object tracking means for tracking the position of objects identified by said object identifying means. Furthermore, the invention relates to a corresponding vision method.
  • Vision systems for motor vehicles must work under a large variety of conditions ranging from clear day or indoors, to heavy snowfall or rain even at night.
  • the reliability of object detection is affected by the surrounding conditions, for example if there is water on a window in front of the camera, and therefore the information available in the image is also affected. This can have a negative influence on the reliabil- ity and/or speed of the tracking means.
  • the tracking means usually operates under the assumption of an average noise level in the recorded images. An actual noise level being higher than the assumed average noise level will result in noisy filter outputs. On the other hand, an actual noise level being lower than the assumed average noise level will result in the filter response being relatively slow.
  • the object of the invention is to provide a vision system and method with a fast and reliable tracking means providing high quality outputs under different surrounding conditions.
  • the invention solves this object with the features of the in- dependent claims.
  • situations can be determined where the image quality in the recorded images is low.
  • Such conditions for example include darkness, overexposure by sunlight and/or rain, snow or fog.
  • the tracking means can be optimally adjusted to the current surrounding conditions, providing tracking outputs with low noise under different external conditions, and avoiding a tracking means which is unnecessarily slow if the external conditions are good.
  • the object tracking means comprises a tracking filter.
  • this may be a recursive filter, in particular a recursive time domain filter like a Kalman filter which has the possibility to specify the expected measurement noise.
  • This parameter is conventionally set to a constant value corresponding to an average expected noise level independent of the actual external conditions.
  • the invention allows using the calculated (variable) information content of the recorded images, reflecting the actual surrounding condition, to be used as the expected measurement noise control parameter of the Kalman filter, or the tracking filter in general.
  • the noise level is calculated using image diagnostics of the recorded image data which allows a reliable esti- mation of the information content in the recorded image data.
  • This may comprise for example a frequency spectrum analysis, e.g. a Fourier transform of the image data and/or an edge strength analysis, e.g. the application of a Sobel operator filter to the image data.
  • Information from a rain sensor and/or information indicating windscreen wiper activation may also be used to estimate the noise level.
  • Fig. 1 shows a vision system for a motor vehicle.
  • the vision system 10 is mounted in a motor vehicle and comprises an imaging means 11 for recording images of a region in front of the motor vehicle.
  • the imaging means 11 comprises one or more optical and/or infrared cameras 12a, 12b, where infrared covers near IR with wavelengths below 5 microns and/or far IR with wavelengths beyond 5 microns .
  • the imaging means 11 comprises stereo cameras 12a, 12b; alternatively a mono camera can be used.
  • the cameras 12a, 12b are coupled to an image pre-processor 13 which may be realized by a dedicated hardware circuit, and is adapted to calibrate the cameras 12a, 12b, control the capture and digitizing of the images, warp them into alignment, merge left/right images into single images, and create multi-resolu- tion disparity images, which per se is known in the art.
  • image pre-processor 13 which may be realized by a dedicated hardware circuit, and is adapted to calibrate the cameras 12a, 12b, control the capture and digitizing of the images, warp them into alignment, merge left/right images into single images, and create multi-resolu- tion disparity images, which per se is known in the art.
  • the pre-processed image data is then provided to an electronic processing means 14 where further image processing is carried out by means of a corresponding software.
  • the processing means 14 comprises an object detection means 15 adapted to identify and classify possible objects in front of the motor vehicle, such as pedestrians, bicyclists or large animals, a tracking means 16 adapted to track the position of object candidates in the recorded images identified by the object detection means 15 over time, and a decision means 17 adapted to activate or control vehicle safety means 18, 19, 20, ... depending on the result of the processing in the object detection and tracking means 15, 16.
  • warning means 18 may comprise a warning means 18 adapted to provide a collision warning to the driver by suitable optical, acoustical and/or haptical warning signals; display means 19 for displaying information relating to an identified object; one or more restraint systems 20 such as occupant airbags or safety belt tensioners,- pedestrian airbags, hood lifters and the like; dynamic vehicle control systems such as brakes.
  • restraint systems 20 such as occupant airbags or safety belt tensioners,- pedestrian airbags, hood lifters and the like
  • dynamic vehicle control systems such as brakes.
  • the object tracking means 16 comprises a tracking filter, e.g. a Kalman filter, which allows to specify an expected measurement noise as a control parameter.
  • a tracking filter e.g. a Kalman filter
  • the electronic processing means 14 is preferably programmed or programmable and may comprise a microprocessor or micro- controller.
  • the image pre-processor 13 and the electronic processing means 14 are preferably realised in an on-board electronic control unit (ECU) and may be connected to the cameras 12a, 12b, the safety means 18, 19, 20, ... and other devices like a vehicle rain sensor 21 or a means 22 indicating windscreen wiper activation via a vehicle data bus. All steps from imaging, image pre-processing, image processing, to activation or control of safety means 18, 19, 20, ... are automatically and continuously performed during driving in real time.
  • ECU electronice control unit
  • the processing means 14 comprises a noise level calculation means 24, preferably realized by software, which is adapted to calculate an estimated noise level of the recorded images, and to input said estimated noise level into the object tracking means 16 for use in the object tracking process performed therein.
  • said estimated noise level is specified as the expected measurement noise control parameter of the Kalman filter.
  • the estimated noise level is calcu- lated using image diagnostics of the recorded image data, which is indicated in Fig. 1 by the solid input arrow to the noise level calculation means 24.
  • image diagnostics of the recorded image data which is indicated in Fig. 1 by the solid input arrow to the noise level calculation means 24.
  • an analysis of the image frequency contents in a detected region-of-interest e.g. using a 2D discrete Fourier transformation
  • an analysis of the image frequency contents in the whole image also e.g. using a 2D discrete Fourier transformation
  • an edge strength analy- sis e.g. using a Sobel operator filter, can be employed, where strong edges indicate a clear view.
  • a clear, sharp view indicates a high information content in the image resulting in low noise in the detected object.
  • an independent imaging means or other dedicated sensing means may be used for obtaining information allowing to calculate the estimated noise level of the surrounding vehicle environment .
  • information from other sources may be used for calculating the estimated noise level.
  • signals from a vehicle rain sensor 21 may be used, where detected rain usually indicates a reduced image quality.
  • Information 22 indicating windscreen wiper activation, providing another indicator for a reduced image quality, may also be used.

Landscapes

  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)
  • Closed-Circuit Television Systems (AREA)
  • Image Processing (AREA)

Abstract

L'invention concerne un système de vision (10) pour véhicule à moteur qui comprend un moyen d'imagerie (11) permettant d'enregistrer des images d'une région devant le véhicule à moteur, un moyen d'identification d'objets (15) permettant de traiter des données d'images fournies par le moyen d'imagerie (11) afin d'identifier des objets dans la scène imagée, et un moyen de suivi d'objets (16) permettant de suivre la position des objets identifiés par ledit moyen d'identification d'objets (15). Ledit système de vision (10) comprend un moyen de calcul du niveau sonore (24) destiné à calculer un niveau sonore estimé des données d'images enregistrées, et à entrer ledit niveau sonore estimé dans ledit moyen de suivi d'objets (16) à utiliser dans ledit suivi d'objets.
PCT/EP2009/004075 2009-06-05 2009-06-05 Systeme et procede de vision pour vehicule a moteur WO2010139351A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/EP2009/004075 WO2010139351A1 (fr) 2009-06-05 2009-06-05 Systeme et procede de vision pour vehicule a moteur

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP2009/004075 WO2010139351A1 (fr) 2009-06-05 2009-06-05 Systeme et procede de vision pour vehicule a moteur

Publications (1)

Publication Number Publication Date
WO2010139351A1 true WO2010139351A1 (fr) 2010-12-09

Family

ID=41666638

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2009/004075 WO2010139351A1 (fr) 2009-06-05 2009-06-05 Systeme et procede de vision pour vehicule a moteur

Country Status (1)

Country Link
WO (1) WO2010139351A1 (fr)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6535114B1 (en) * 2000-03-22 2003-03-18 Toyota Jidosha Kabushiki Kaisha Method and apparatus for environment recognition

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6535114B1 (en) * 2000-03-22 2003-03-18 Toyota Jidosha Kabushiki Kaisha Method and apparatus for environment recognition

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
BAR-SHALOM YU ET AL: "USE OF MEASUREMENTS FROM AN IMAGING SENSOR FOR PRECISION TARGET TRACKING", IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 25, no. 6, 1 November 1989 (1989-11-01), pages 863 - 871, XP000096181, ISSN: 0018-9251 *
HANDMANN U., LORENZ G., SCHNITGER T., VON SEELEN W.: "Fusion of Different Sensors and Algorithms for Segmentation", IEEE INTERNATIONAL, 1998, XP002572171 *
OKTEM R ET AL: "TRANSFORM DOMAIN APPROACHES FOR IMAGE DENOISING", JOURNAL OF ELECTRONIC IMAGING, SPIE / IS & T, vol. 11, no. 2, 1 April 2002 (2002-04-01), pages 149 - 156, XP001115908, ISSN: 1017-9909 *
QIANG LI ET AL: "Precision tracking of overlapping small targets", SPEECH, IMAGE PROCESSING AND NEURAL NETWORKS, 1994. PROCEEDINGS, ISSIP NN '94., 1994 INTERNATIONAL SYMPOSIUM ON HONG KONG 13-16 APRIL 1994, NEW YORK, NY, USA,IEEE, 13 April 1994 (1994-04-13), pages 41 - 44, XP010121562, ISBN: 978-0-7803-1865-6 *
TAI, S.-C., YANG S.-M.: "A Fast Method For Image Noise Estimation Using Laplacian Operatror and Adaptive Edge Detection", ISCCSP, 12 March 2008 (2008-03-12) - 14 March 2008 (2008-03-14), pages 1077 - 1081, XP002572170 *

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