Dumortier, Yann (2009) Monocular environment perception for intelligent transport systems. PhD thesis Informatique temps - réel, robotique, automatique, CAOR-Centre de Robotique, ENSMP p.157.

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Abstract

The evolution of transport in recent decades, demonstrates an ongoing commitment to reduce the constraints associated with the concept of displacement. To this end, an important part of efforts intended to reduce the travel time, mainly through the improvement of infrastructure and alternative transportation. The multi-modal transportation planning, expected to meet different needs of users, was not enough to stop the rise of the automobile in urban areas. Thus, cars are gradually become the main source of pollution and urban accidents. The solutions studied to remedy this situation are mainly based on the responsibility of the human factor. They essentially offer to replace the automobile by autonomous transport system. The automation of vehicles, gradually established by the Advanced Driving Assistance Systems, requires the development of modules for environmental perception that analyze and process information gained from one or more sensors. With the boom of computational capabilities embedded systems, the camera has become one of the most used sensors, as long for the wealth of information contained in a sequence of images, for its low cost and small footprint. The work presented in this thesis provides a novel solution to the problem of visual perception for automatic driving through a monocular approach based on the study of geometric constraints applied to motion picture.

References

[1] J.-Y. Bouguet, "Pyramidal implementation of the Lucas Kanade feature tracker

: Description of the algorithm", in Intel Research Laboratory, Technical

Report (1999).

[2] T. Fraichard and H. Asama, "Inevitable collision states. A step towards safer

robots ?", in Proceedings of the IEEE-RSJ International Conference on

Intelligent Robots and Systems (2003), pp. 388-393.

[3] S. Petti and T. Fraichard, "Safe navigation of a car-like robot in a dynamic

environment", in Proceedings of the European Conference on Mobile Robots

(2005).

[4] Y. Ma, S. Soatto, J. Kosecká and S. Shankar Sastry, "An invitation to 3-

D vision : from images to geometric models", in Interdisciplanary applied

mathematics (2000), Vol. 26, Springer ed..

[5] J. E. Cutting and P. M. Vishton The, "Perceiving layout and knowing distances

: the integration, relative potency, and contextual use of different

information about depth", in Perception of Space and Motion (1995), pp.

69–117, Academic Press.

[6] I. P. Howard, and B.J. Rogers, "Binocular Vision and Stereopsis", in Oxford

University Press (1995).

[7] B. Rogers, and M. Graham, "Motion parallax as an independent cue for

depth perception", in Perception (1979), No 8, pp. 125–134.

[8] J. Zhou and B. Li, "Robust ground plane detection with normalized homography

in monocular sequences from a robot platform", in Proceedings of

the IEEE International Conference on Image Processing (2006).

[9] A. Wedel, T. Schoenemann, T. Brox and D. Cremers, "WarpCut - Fast

obstacle segmentation in monocular video", in Lecture Notes in Computer

Science (2007), pp. 264-273, Springer ed..

[10] W. Trobin, T. Pock, D. Cremers and H. Bischof, "An unbiased second-order

prior for high accuracy motion estimation", in DAGM Symposium (2008),

pp. 396-405.

[11] D. Aubert and C. Thorpe, "Color image processing for navigation : two road

trackers", in CMU Robotics Institute Laboratory, Technical Report (1990).

[12] S. Beucher and M. Bilodeau, "Road segmentation and obstacle detection

by a fast watershed transformation", in Proceedings of the IEEE Intelligent

Vehicles Symposium (1994), pp. 296-301.

[13] A. Broggi and S. Berte, "Vision-based road detection in automotive systems

: a real-time expectation driven approach", in Journal of Artificial Intelligence

Research (1995), vol. 3, pp. 325-348.

[14] F. Paetzold and U. Franke, "Road recognition in urban environment", in

Image and Vision Computing (2000), vol. 18, pp. 377-387.

[15] T. M. Huang, V. Kecman and I. Kopriva, "Kernel based algorithms for mining

huge data sets, supervised, semi-supervised, and unsupervised learning",

in Springer-Verlag (2006).

[16] A. Khammari, F. Nashashibi, Y. Abramson and C. Laurgeau, "Vehicle detection

combining gradient analysis and AdaBoost classification", in Proceedings

of the IEEE International Conference on Intelligent Transportation

Systems (2005).

[17] M. A. Sotelo, I. Parra, D. Fernández and E. Naranjo, "Pedestrian detection

using SVM and multi-feature combination", in Proceedings of the IEEE International

Conference on Intelligent Transportation Systems (2006).

[18] D. Hoiem, A. A. Efros and M. Hebert, "Closing the loop in scene interpretation",

in Proceedings of the IEEE computer society conference on Computer

Vision and Pattern Recognition (2008).

[19] N. Soquet and D. Aubert, "Road Segmentation Supervised by an Extended

V-Disparity Algorithm for Autonomous Navigation", in Proceedings of the

IEEE Intelligent Vehicles symposium (2007).

[20] N. Simond and M. Parent, "Obstacle detection from IPM and Super-

Homography", in Proceedings of the IEEE International Conference on Intelligent

Robots and Systems (2007).

[21] M. Yang, Q. Yu, H. Wang and B. Zhang, "Vision-based real-time obstacle

detection and tracking for autonomous vehicle guidance", in. SPIE Real-

Time Imaging VI, Vol 4666, pp. 65-74, Nasser Kehtarnavaz Ed.

[22] W. Miled, J.-C. Pesquet and M. Parent, "Robust obstacle detection based on

dense disparity maps", in the Eleventh International Conference on Computer

Aided Systems Theory (2007).

[23] S. Lefebvre, S. Ambellouis and F. Cabestaing, "Obstacles detection on a

road by dense stereovision with 1D correlation windows and fuzzy filtering",

in Proceedings of the IEEE International Conference on Intelligent Transportation

Systems (2006).

[24] N. B. Touzene, and S. Larabi, "Obstacle detection from uncalibrated cameras",

in Proceedings of the IEEE Panhellenic Conference on Informatics

(2008).

[25] R. Labayrade, and D. Aubert, "In-vehicle obstacles detection and characterization

by stereovision", in Proceedings of the IEEE International Conference

on Cognitive Computer Vision Systems (2003).

[26] J. Wang , Z. Hu, H. Lu and K. Uchimura, "Motion detection in driving

environment using U-V-disparity", in Lecture Notes in Computer Science

(2006), pp. 307-316, Springer : Berlin.

[27] Y. Gao, "Etude psychophysiologique de la vision en relief humaine en télévision

stéréo", Thèse de Génie Biologique et Médical à l’institut national

des sciences appliquées de Lyon (1992).

[28] H. H. Nagel, "Extending oriented smoothness constraint into the temporal

domain and the estimation of derivatives of optical flow", in Lecture Notes

in Computer Science (1990), pp. 139-148, Springer : Berlin.

[29] M. J. Black, "Robust incremental optical flow", in Ph.D. thesis (1992), Yale

University.

[30] C. Koch, H. T.Wang, B. Mather, A. Hsu and H. Suarez, "Computing optical

flow in resistive networks and in the primate visual system", in Proceedings

of the IEEE Workshop on Visual Motion (1989), pp. 62-72.

[31] H. H. Nagel, "On the estimation of optical flow relations between different

approaches and some new results", in Articial Intelligence (1987), No. 33,

pp. 299-324.

[32] A. D. Jepson and M. Black, "Mixture models for optical flow computation.",

in IEEE Computer Society. Conference on Computer Vision and Pattern

Recognition (1993), pp. 760-761.

[33] A. Singh, "Incremental estimation of image flow using Kalman filter", in

Proceedings of the IEEE Workshop on Visual Motion (1991), pp. 36-43.

[34] B. K. P. Horn and B. G. Schunck, "Determining optical flow", in Artificial

Intelligence (1981), No. 17, pp. 185-204.

[35] J. L. Barron, D. J. Fleet and S. S. Beauchemin, "Performance of Optical

Flow Techniques", in International Journal of Computer Vision (1994),

Vol. 12, No. 1, pp. 43-77, Springer : US.

[36] B. Lucas and T. Kanade, "An iterative image registration technique with an

application to stereovision", in Proceedings of the DARPA IU Workshop

(1981), pp. 121-130.

[37] S. Beauchemin and J. Barron, "The computation of optical flow", in ACM

Computing Surveys (1995), Vol. 27, No. 3, pp. 433–467.

[38] D. Pellerin, A. Spinéi and A. Guérin-Dugué, "Calcul du flot optique par filtrages

de Gabor combinés", in Traitement du Signal (1996), Vol. 13, No. 1.

[39] D. J. Heeger, "Optical flow using spatio-temporal filters", in International

Journal of Computer Vision (1988), Vol. 1, pp. 279-302.

[40] V. Argyriou and T. Vlachos, "A study of sub-pixel motion estimation using

phase correlation", in Technical Report (2004), University of Surrey.

[41] C. D. Kuglin and D. C. Hines, "The phase correlation image alignment

method", in Proceedings of the Conference Cybernetics Society (1975),

pp. 163-165.

[42] Y. T. Wu, T. Kanade, J. Cohn and C. C. Li, "Optical flow estimation using

wavelet motion model", in Proceedings of the Sixth International Conference

on Computer Vision (1998).

[43] R. Szeliski and H.-Y. Shum, "Motion Estimation with Quadtree Splines", in

Technical Report (1995), Cambridge Research Lab.

[44] C. Bernard, "Discrete Wavelet Analysis for Fast Optic Flow Computation",

in Applied and Computational Harmonic Analysis (2001), Vol. 11, No. 1,

juillet 2001, pp. 32-63.

[45] W. Li and E. Salari, "Successive elimination algorithm for motion estimation",

in IEEE Transactions on Image Processing (1995), Vol. 4, No. 1.

[46] X. Jing and L. P. Chaud, "An efficient three-step search algorithm for block

motion estimation", in IEEE Transactions on Multimedia (2004), Vol. 6,

No. 3.

[47] Y. Nie and K. K. Ma, "Adaptive rood pattern search for fast block matching

motion estimation", in IEEE Transactions on Image Processing (2002),

Vol. 11, No. 12.

[48] F. Essannouni, R. O. H. Thami, A. Salam and D. Aboutajdine, "An efficient

fast full search block matching algorithm using FFT algorithms", in International

Journal of Computer Science and Network Security (2006), Vol. 6,

No. 3b.

[49] "H.264 : Advanced video coding for generic audiovisual services", in Technical

Report (2003), International Telecommunication Union, http://www.

itu.int/rec/T-REC-H.264-200305-S

[50] C. Q. Davis, Z. Z. Karul, D. M. Freeman, "Equivalence of subpixel motion

estimators based on optical flow and block matching", in International Symposium

on Computer Vision (1995).

[51] J. Barron and R. Klette, "Quantitative Color Optical Flow", in 16th International

Conference on Pattern Recognition (2002), Vol. 4, pp. 251–255.

[52] P. Golland and A. M. Bruckstein, "Motion from color", in Technical Report

#9513 (1997), Computer Science Department, Technion.

[53] J. Marzat, "Estimation temps réel du flot optique", in Rapport de Stage Ingénieur

(2008), Institut National de Recherche en Informatique et Automatique

(2008).

[54] J. Marzat, Y. Dumortier and A. Ducrot, "Real-time Dense and Accurate Parallel

Optical Flow using CUDA", International Conference in Central Europe

on Computer Graphics, Visualization and Computer Vision (WSCG

2009).

[55] E. Mémin, "Estimation du flot-optique : contributions et panorama de différentes

approches", in Habilitation à Diriger des Recherches (2003), Université

Rennes 1, Institut de Formation Supérieure en Informatique et en

Communication.

[56] M. Orkisz and P. Clarysse, "Estimation du flot optique en présence de discontinuités

: une revue", in Traitement du Signal (1996), Vol. 13(5), pp 489-

513.

[57] R. Deriche and O. Faugeras, "Les EDP en traitement des images et vision

par ordinateur", in Rapport de Recherche (1995), Institut National de Recherche

en Informatique et Automatique.

[58] D. A. Danielson, "Vectors and tensors in engineering and physics", second

edition (2003), in Advanced Book Program, Westview Press.

[59] G. Medioni and M. S. Lee, "A computational framework for segmentaion

and grouping", (2000), Elsevier edition.

[60] M. Nicolescu and G. Medioni, "4-D voting for matching, densification and

segmentation into motion layers", in International Conference on Pattern

Recognition (2002), Vol. 3, pp. 303-308.

[61] Y. Dumortier, I. Herlin and A. Ducrot, "4-D Tensor Voting motion segmentation

for obstacle detection in autonomous guided vehicle", in IEEE Intelligent

Vehicles Symposium (2008), pp. 379-384.

[62] Y. Dumortier, I. Herlin, "Monocular Moving Obstacle Detection for Autonomous

Guided Vehicles", in Transport Research Arena, Young European

Arena of Research (2008).

[63] K. Mullet and D. Sano, "Designing visual interfaces : Communication

oriented techniques", (1995), Englewood Cliffs, NJ : Prentice Hall.

[64] B. Giai-Checa, P. Bouthemy and T. Vieville, "Segment-based detection of

moving objects in a sequence of images", in International Conference on

Pattern Recognition (1994), pp. 384-389.

[65] G. Csurka and O. Faugeras, "Algebraic and geometrical tools to compute

projective and permutation invariants", in IEEE Trans. Pattern Analysis and

Machine Intelligence (1999), Vol. 21, pp. 58-65.

[66] C. Cappelle, M. El Badaoui El Najjar, F. Charpillet and D. Pomorski, "Obstacle

detection and localization method based on 3D model : Distance validation

with ladar", in IEEE International Conference on Robotics and Automation

(2008), pp. 4031-4036.

[67] Z. Zhang, M. Li, K. Huang and T. Tan, "3D Model based Vehicle Localization

by Optimizing Local Gradient based Fitness Evaluation", int IEEE

International Conference on Pattern Recognition (2008), pp. 1-4.

[68] A. Broggi and S. Berte, "Vision-based road detection in automotive systems

: a real-time expectation driven approach", in Journal of Artificial Intelligence

Research (1995), Vol. 3, pp. 325-348.

[69] Y. Li, K. He and P. J. Tsinghua, "Road markers recognition based on shape

information", in IEEE Intelligent Vehicles Symposium (2007), pp 117-122.

[70] A. Kuehnle, "Symmetry-based recognition of vehicle rears", in Pattern Recognition

Letters (1991), pp. 249-258.

[71] T. Zielke, M. Brauckmann and W. von Seelen, "Intensity and edge-based

symmetry detection with an application to car-following", in Graphical Models

and Image Processing (1993), No. 2, pp. 177-190.

[72] Y. He, H. Wang and B. Zhang, "Color-based road detection in urban trafic

scenes", in IEEE Intelligent Transportation Systems (2004), Vol. 5, pp. 309-

318.

[73] P. Paalanen, V. Kyrki and J.-K. Kamarainen, "Towards monocular on-line

3D reconstruction", in Workshop on Vision in Action : Efficient strategies

for cognitive agents in complex environments (2008).

[74] T.L. Gandhi, S. Devadiga, R. Kasturi and O.I. Camps, "Detection of obstacles

on runway using ego-motion compensation and tracking of significant

features", in Proceedings or the 3rd IEEE Workshop on Applications

of Computer Vision (1996), pp. 168-173.

[75] S. Beucher and F. Meyer, "The morphological approach to segmentation :

the watershed transformation", in Mathematical Morphology in Image Processing

(1993), Ed. E.R. Dougherty, pp. 433-481.

[76] L. Najman, M. Couprie and G. Bertrand, "Watersheds, mosaics, and the

emergence paradigm", in Discrete Applied Mathematics (2005), Vol. 147,

No 2-3, pp. 301-324 .

[77] J.C. Klein, "Conception et réalisation d’une unité logique pour l’analyse

quantitative d’images", in Ph.D. thesis (1976), Université de Nancy.

[78] J. Serra and P. Salembier, "Connected operators and pyramids", in Image

Algebra and Mathematical Morphology (1993), Vol. 2030, pp. 65-76,

ed. SPIE.

[79] P. Salembier and J. Serra, "Flat zones filtering, connected operators and

filters by reconstruction", in IEEE Transactions on Image Processing (1995),

Vol. 3, No. 8, pp. 1153-1160.

[80] L. Vincent, "Morphological gray scale reconstruction in image analysis applications

and efficients algorithms", in IEEE Transactions on Image Processing

(1993), Vol. 2, No. 2, pp. 176-201.

[81] F. Meyer and S. Beucher, "Morphological segmentation", in Journal of Visual

Communication and Image Representation (1990), Vol. 1, No. 1, pp. 21-

46

[82] P. Salembier, L. Torres, F. Meyer and C. Gu, "Region-based video coding

using mathematical morphology", in Proceedings of IEEE (papier invité)

(1995), Vol 83, No. 6, pp. 843-857.

[83] C. Vachier, "Utilisation d’un critère volumique pour le filtrage d’image",

in 11th Conference on Shape recognition and artificial intelligence (1998),

pp. 307-315.

[84] J.-Y. Bouguet, "Camera Calibration Toolbox for Matlab", http://www.

vision.caltech.edu/bouguetj/calib_doc/.

[85] Z. Zhang, "A flexible new technique for camera calibration", in IEEE

Transactions on Pattern Analysis and Machine Intelligence (2000), 22(11),

pp. 1330-1334.

[86] Z. Zhang, "Flexible Camera calibration by viewing a plane from unknown

orientations", in International Conference on Computer Vision (1999),

pp. 666-673.

[87] L. S. Davis, D. Oberkampf and D. Dementhon, "Iterative pose estimation

using coplanar feature points", in Computer Vision and Image Understanding

(1996), 63(3), pp. 495-511.

[88] D. DeMenthon and L. S.Davis, "Model-based object pose in 25 lines of

code", in European Conference on Computer Vision (1992), pp. 335-343.

[89] Y. Dumortier, M. Kais and R. Benenson, "Real-time vehicle motion estimation

using texture learning and monocular vision", in International Conference

on Computer Vision and Graphics (2006).

[90] O. Faugeras and F. Lustman, “Motion and structure from motion in a piecewise

planar environment”, in International Journal of Pattern Recognition

and Artificial Intelligence (1988), 2(3), pp. 485–508.

[91] O. Faugeras, "Three-dimensional computer vision : a geometric viewpoint",

in MIT Press (1993).

[92] Z. Zhang, and A.R. Hanson, “3D Reconstruction based on homography

mapping”, in Proceedings of ARPA (1996), pp. 1007-1012.

[93] A. Agarwal, C. V. Jawahar and P. J. Narayanan, "A survey of planar homography

estimation techniques", in technical report of Indian International

Institute of Information Technology (2005).

[94] E. Malis and M. Vargas, "Deeper understanding of the homography decomposition

for vision-based control", in Rapport de Recherche de l’INRIA

(2007).

[95] M. Irani and P. Anadan, "Parallax geometry of pairs of points for 3-D scene

analysis", in European Conference on Computer Vision (1996), pp. 17-30.

[96] L. Seiler, et al., "Larrabee : a many-core x86 architecture for visual computing",

in ACM Transaction on Graphics (2008), Vol. 27, No. 3, pp. 1-15.

[97] "NVIDIA CUDA : Compute Unified Device Architecture. Programming

guide", in NVIDIA technical report (2008).

[98] T. R. Halfhill, "Parallel Processing with CUDA", in Microprocessor Report

(2008).

[99] R. Hadsell, P. Sermanet, M. Scoffier, A. Erkan, K. Kavackuoglu, U. Muller

and Y. LeCun, "Learning long-range vision for autonomous off-road driving",

in Journal of Field Robotics (2009), Vol. 2, No. 26, pp. 120-144.

[100] R. Hartley and A. Zisserman, "Multiple view geometry in computer vision",

in Cambridge University Press (2000).

[101] G. W. Stewart, "On the early history of the singular value decomposition",

in technical report of University of Maryland (1992).

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