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artificial PErception under Adverse CONditions: The Case of the Visibility Range

Young Researchers Seminar 2007 Brno, Czech Republic, 27 to 30 May 2007. artificial PErception under Adverse CONditions: The Case of the Visibility Range LCPC in cooperation with INRETS , France. Nicolas Hautière. Overview. ADAS and adverse visibility conditions

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artificial PErception under Adverse CONditions: The Case of the Visibility Range

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  1. Young Researchers Seminar 2007 Brno, Czech Republic, 27 to 30 May 2007 artificial PErception under Adverse CONditions: The Case of the Visibility Range LCPC in cooperation with INRETS, France Nicolas Hautière

  2. Overview • ADAS and adverse visibility conditions • Visibility range under daytime fog • Modelling • Measurement methods • Experimental validation • Under way applications • Discussion and future works PErception under Adverse CONditions Nicolas Hautière, LCPC

  3. ADAS and Adverse Visibility Conditions To detect the visibility conditions allows: Human aspects • To switch or to adapt the operation of vehicle lights (AFS) • To adapt the speed according to the weather conditions (ISA) Sensor aspects • To improve the operation range of exteroceptive sensors • To qualify / adapt / stop the other driving assistances Improve the safety ! Let’s talk about daytime fog [Hautière and Aubert, 2005a] Hautière, N. and Aubert, D. (2005). Onboard evaluation of the atmospheric visibility for driving assistance systems, Recherche Transports Sécurité, 87:89-108. PErception under Adverse CONditions Nicolas Hautière, LCPC

  4. Koschmieder’s Law: Let express the contrast of an object against the sky:  Contrast attenuation Daylight Visibility Range under Daytime Fog Atmospheric veil Scattering Direct transmission • For a black object (C0=1) and a visibility contrast threshold of 5%: “the greatest distance at which a black object of suitable dimensions can be recognized by aday against the horizon sky” (CIE, 1987) PErception under Adverse CONditions Nicolas Hautière, LCPC

  5. u C x vh f z v y Image plane H q Measurement and derivation of intensity curveExtraction of the inflection point B&W camera S Road plane X M Z d Y Estimation of the meteorological visibility distance Extraction of a region of interestFitting of a measurement bandwidth Vmet = 50m Exploitation of the Atmospheric Veil Method: Instanciation of Koschmieder’s Law Assuming a flat road: [Hautière et al., 2006a] Hautière, N., Tarel, J.-P, Lavenant, J. and Aubert, D. (2006). Automatic Fog Detection and Measurement of the visibility Distance through use of an Onboard Camera. Machine Vision Applications Journal, 17(1):8-20 vh horizon line,  camera parameters PErception under Adverse CONditions Nicolas Hautière

  6. Local contrast measurement based on a binarisation method: where f(x) • Evaluated contrast on F(s) is equal to 2C(s0) F(s) s Cx,x1(s) f(x1) x x1 Exploitation of Contrast Attenuation Method: computation of the range to the most distant visible object • Estimation of the so-called mobilized visibility distance • Range map obtained by “v-disparity” stereovision approach • Visible objects are those having a local contrast above 5% [Hautière et al., 2006b] Hautière, N., Labayrade, R. and Aubert, D. (2005). Real-Time Disparity Contrast Combination for Onboard Estimation of the Visibility Distance. IEEE Transactions on Intelligent Transportation Systems, 7(2):201-212. PErception under Adverse CONditions Nicolas Hautière, LCPC

  7. Video samples Daytime fog Twilight fog PErception under Adverse CONditions Nicolas Hautière, LCPC

  8. Experimental Validation Development of a validation site • Objectives : • To estimate Vmet thanks to the targets: • To obtain a ground truth, • To compare it with the in-vehicle methods • Triangle based pattern • Constant solid angle • 5 fixed targets: • d=65m 1mx1m • d=98m 1.5mx1.5m • d=131m 2mx2m • d=162m 2.5mx2.5m • d=195m 3mx3m • 1 mobile targe: 0.5mx0.5m PErception under Adverse CONditions Nicolas Hautière, LCPC

  9. Content Experimental Validation Sample images of the validation site Sunny weather Vmet= 5000 m Light rain Vmet= 3400 m Haze Vmet= 2130 m Fog Vmet= 255 m Thick fog Vmet= 61 m Snow fall Vmet= 1000 m PErception under Adverse CONditions Nicolas Hautière, LCPC

  10. Experimental Validation Quantitative results Meteorological visibility estimation Mobilized visibility distance estimation [Hautière et al., 2006c] Hautière, N., Aubert, D., Dumont, E. and Tarel, J.-P. (2008).Experimental Validation of Dedicated Methods to In-Vehicle Estimation of Atmospheric Visibility. IEEE Transactions on Instrumentation and Measurement, 57(10), 2218-2225. PErception under Adverse CONditions Nicolas Hautière, LCPC

  11. Under Way Applications Improved Road Departure Prevention • Principle: reversal of Koschmieder’s law • Assuming a flat world, we have: d1=28m d2=62m Enhancement of road markings extraction under adverse visibility conditions [Hautière and Aubert, 2005b] Hautière, N. and Aubert, D. (2005). Contrast Restoration of Foggy Images through use of an Onboard Camera, IEEE Conference on Intelligent Transportation Systems (ITSC’05), Vienna, Austria PErception under Adverse CONditions Nicolas Hautière, LCPC

  12. Contrast enhancement of the road scene Iterative contrast restoration Under Way Applications Improved obstacle detection [Hautière et al., 2007] Hautière, N., Tarel, J.-P., Aubert, D. (2007).Towards Fog-Free In-Vehicle Vision Systems through Contrast Restoration. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’07), Minneapolis, USA PErception under Adverse CONditions Nicolas Hautière, LCPC

  13. Under Way Applications ISA and “risk mitigation” or “safety margin” • Objective: to compute an adequate speed according to the weather conditions based solely on a digital map and a camera • One of the requirements: a mapping function between the driver and the sensor visions: [Hautière and Aubert, 2006d] Hautière, N. and Aubert, D. (2006). Visible Edges Thresholding: a HVS based Approach, International Conference on Pattern Recognition (ICPR’06), Hong-Kong, China PErception under Adverse CONditions Nicolas Hautière, LCPC

  14. We have presented two methods, issued from the ARCOS French project, to estimate the visibility range and some applications, Methods are being extended in the REACT project by the Mines de Paris to develop probe vehicles, Currently, we are adapting the methods to the use of fixed CCTV cameras in the SAFESPOT IP, In the future, we would like to apply our scientific processes to other adverse visibility conditions, like: Rain Glare Nocturnal Fog Discussion, Current and Future works  This is the heart of the FP7 PEACON proposal lead by LCPC/INRETS ! PErception under Adverse CONditions Nicolas Hautière, LCPC

  15. I would like to acknowledge the contributions of my colleagues Didier Aubert, Jean-Philippe Tarel, Raphaël Labayrade, Benoit Lusetti, Eric Dumont from LCPC and INRETS, of Michel Jourlin from the University of Saint-Etienne, and of Clément Boussard from the Mines Paris. Acknowledgments PErception under Adverse CONditions Nicolas Hautière, LCPC

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