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Driver-Environment Understanding

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Computer Vision for Driver Assistance

Part of the book series: Computational Imaging and Vision ((CIVI,volume 45))

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Abstract

This book focuses in particular on driver-environment understanding as briefly outlined at the end of the previous chapter. This chapter provides a more detailed introduction, motivations, and a review of the state-of-the-art in this area of vision-based driver-assistance systems. The chapter also discusses existing challenges and outlines the structure of the book.

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Notes

  1. 1.

    www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/

  2. 2.

    They are available at www.d2.mpi-inf.mpg.de/node/428 and ccv.wordpress.fos.auckland.ac.nz/data/object-detection/ for free download.

Bibliography

  1. 6D Vision (2014), www.6d-vision.com

  2. A. Ali, S. Afghani, Shadow based on-road vehicle detection and verification using Haar wavelet packet transform, in Proceedings of the IEEE Conference on Information Communication Technologies (2005), pp. 346–350

    Google Scholar 

  3. J.M. Alvarez, A.M. Lopez, T. Gevers, F. Lumbreras, Combining priors, appearance and context for road detection. IEEE Trans. Intell. Transp. Syst. 15, 1168–1178 (2014)

    Article  Google Scholar 

  4. H. Badino, U. Franke, D. Pfeiffer, The stixel world – a compact medium level representation of the 3D-world, in Proceedings of the DAGM – Pattern Recognition (2009), pp. 51–60

    Google Scholar 

  5. A. Bar Hillel, R. Lerner, D. Levi, G. Raz, Recent progress in road and lane detection: a survey. Mach. Vis. Appl. 25, 727–747 (2014)

    Article  Google Scholar 

  6. N. Barnes, A. Zelinsky, Real-time radial symmetry for speed sign detection, in Proceedings of the IEEE Intelligent Vehicles Symposium (2004), pp. 566–571

    Google Scholar 

  7. A. Barth, Vehicle tracking and motion estimation based on stereo vision sequences. PhD thesis, Bonn University, 2010

    Google Scholar 

  8. R. Basri, D.W. Jacobs, Lambertian reflectance and linear subspaces. IEEE IEEE Trans. Pattern Anal. Mach. Intell. 25, 218–233 (2003)

    Article  Google Scholar 

  9. J. Batista, A drowsiness and point of attention monitoring system for driver vigilance, in Proceedings of the IEEE Conference on Intelligent Transportation Systems (2007), pp. 702–708

    Google Scholar 

  10. L.M. Bergasa, J. Nuevo, M.A. Sotelo, R. Barea, M.E. Lopez, Real-time system for monitoring driver vigilance. IEEE Trans. Intell. Transp. Syst. 7, 63–77 (2006)

    Article  Google Scholar 

  11. A. Borkar, M. Hayes, M.T. Smith, An efficient method to generate ground truth for evaluating lane detection systems, in Proceedings of the IEEE International Conference on Acoustics Speech Signal Processing (2010), pp. 1090–1093

    Google Scholar 

  12. L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  MATH  Google Scholar 

  13. S.G. Charlton, P.H. Baas, Fatigue, work-rest cycles, and psychomotor performance of New Zealand truck drivers. N. Z. J. Psychol. 30, 32–39 (2006)

    Google Scholar 

  14. J. Crisman, C. Thorpe, Unscarf: a color vision system for the detection of unstructured roads, in Proceedings of the IEEE Conference on Robotics Automation, vol. 3 (1991) pp. 2496–2501

    Google Scholar 

  15. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in Proceedings of the IEEE Computer Vision Pattern Recognition (2005), pp. 886–893

    Google Scholar 

  16. D. Dementhon, L. Davis, Model-based object pose in 25 lines of code. Int. J. Comput. Vis. 15, 123–141 (1995)

    Article  Google Scholar 

  17. P. Dollar, C. Wojek, B. Schiele, P. Perona, Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34, 743–761 (2012)

    Article  Google Scholar 

  18. T. D’Orazio, M. Leo, C. Guaragnella, A. Distante, A visual approach for driver inattention detection. Pattern Recognit. 40, 2341–2355 (2007)

    Article  MATH  Google Scholar 

  19. A. Doshi, M.M. Trivedi, Head and gaze dynamics in visual attention and context learning, in Proceedings of the IEEE Computer Vision Pattern Recognition Workshops (2009), pp. 77–84

    Google Scholar 

  20. DPM Virtual-World Pedestrian Dataset (CVC-07), Computer Vision Center, Universitat Autoǹoma de Barcelona (2014), www.cvc.uab.es/adas/site/?q=node/7

  21. S. Escalera, X. Barò, O. Pujol, J. Vitrià, P. Radeva, Traffic-Sign Recognition Systems (Springer, London, 2011)

    Book  Google Scholar 

  22. P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)

    Article  Google Scholar 

  23. L. Fletcher, A. Zelinsky, Driver inattention detection based on eye gaze—road event correlation. Int. J. Robot. Res. 28, 774–801 (2009)

    Article  Google Scholar 

  24. Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, in Proceedings of the European Conference on Computational Learning Theory (1995), pp. 23–37

    Google Scholar 

  25. G.D. Furman, A. Baharav, C. Cahan, S. Akselrod, Early detection of falling asleep at the wheel: a heart rate variability approach, in Proceedings of the Computers in Cardiology (2008), pp. 1109–1112

    Google Scholar 

  26. T. Gandhi, M.M. Trivedi, Pedestrian protection systems: issues, survey, and challenges. IEEE Trans. Intell. Transp. Syst. 8, 413–430 (2007)

    Article  Google Scholar 

  27. I. Garcia, S. Bronte, L.M. Bergasa, N. Hernandez, B. Delgado, M. Sevillano, Vision-based drowsiness detector for a realistic driving simulator, in Proceedings of the IEEE Conference on Intelligent Transportation Systems (2010), pp. 887–894

    Google Scholar 

  28. D. Geronimo, A.M. Lopez, Vision-Based Pedestrian Protection Systems for Intelligent Vehicles. Springer Briefs in Computer Science (Springer, New York, 2013)

    Google Scholar 

  29. A. Haselhoff, A. Kummert, G. Schneider, Radar-vision fusion for vehicle detection by means of improved Haar-like feature and AdaBoost approach, in Proceedings of the European Association Signal Processing (2007), pp. 2070–2074

    Google Scholar 

  30. O. Jesorsky, K.J. Kirchberg, R.W. Frischholz, Robust face detection using the Hausdorff distance, in Proceedings of the International Conference on Audio-and Video-Based Biometric Person Authentication (Springer, Berlin/Heidelberg, 2001), pp. 90–95

    Book  Google Scholar 

  31. X. Jie, H. Chen, W. Ding, C. Zhao, J. Morris, Robust optical flow for driver assistance, in Proceedings of the Image and Vision Computing New Zealand (2010), pp. 1–7

    Google Scholar 

  32. H. Jing, S.R. Kumar, M. Mitra, Z. Wei-Jing, R. Zabih, Image indexing using color correlograms, in Proceedings of the IEEE Computer Vision Pattern Recognition (1997), pp. 762–768

    Google Scholar 

  33. B. Jun, D. Kim, Robust face detection using local gradient patterns and evidence accumulation. Pattern Recognit. 45, 3304–3316 (2012)

    Article  Google Scholar 

  34. A. Kasinski, A. Schmidt, The architecture and performance of the face and eyes detection system based on the Haar cascade classifiers. Pattern Anal. Appl. 13, 197–211 (2010)

    Article  MathSciNet  Google Scholar 

  35. Z. Kim, Robust lane detection and tracking in challenging scenarios. IEEE Trans. Intell. Transp. Syst. 9, 16–26 (2008)

    Article  Google Scholar 

  36. The KITTI Vision Benchmark Suite (2013), www.cvlibs.net/datasets/kitti/

  37. R. Klette, Concise Computer Vision: An Introduction into Theory and Algorithms (Springer, London, 2014)

    Book  MATH  Google Scholar 

  38. R. Klette, N. Krüger, T. Vaudrey, K. Pauwels, M. van Hulle, S. Morales, F. Kandil, R. Haeusler, N. Pugeault, C. Rabe, M. Lappe, Performance of correspondence algorithms in vision-based driver assistance using an online image sequence database. IEEE Trans. Veh. Technol. 60, 2012–2026 (2011)

    Article  Google Scholar 

  39. L. Kneip, M. Chli, R. Siegwart, Robust real-time visual odometry with a single camera and an IMU, in Proceedings of British Machine Vision Conference (2011), pp. 16.1–16.11

    Google Scholar 

  40. K. Lee, J. Ho, D. Kriegman, Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27, 684–698 (2005)

    Article  Google Scholar 

  41. Y. Lin, F. Guo, S. Li, Road obstacle detection in stereo vision based on UV-disparity. J. Inf. Comput. Sci. 11, 1137–1144 (2014)

    Article  Google Scholar 

  42. M. Ljung, H. Fagerlind, P. Lövsund, J. Sandin, Accident investigations for active safety at CHALMERS – new demands require new methodologies. Veh. Syst. Dyn. 45, 881–894 (2007)

    Article  Google Scholar 

  43. C. Long, X. Wang, G. Hua, M. Yang, Y. Lin, Accurate object detection with location relaxation and regionlets relocalization, in Proceedings of Asian Conference of Computer Vision (2014), pp. 260–275

    Google Scholar 

  44. A.M. Lopez, J. Serrat, C. Canero, F. Lumbreras, T. Graf, Robust lane markings detection and road geometry computation. Int. J. Automot. Technol. 11, 395–407 (2010)

    Article  Google Scholar 

  45. M.J. Lyons, S. Akamatsu, M. Kamachi, J. iro Gyoba, The Japanese female facial expression database (2013), www.kasrl.org/jaffe.html

  46. A.M. Malla, P.R. Davidson, P.J. Bones, R. Green, R.D. Jones, Automated video-based measurement of eye closure for detecting behavioral microsleep, in Proceedings of IEEE International Conference on Engineering Medicine Biology Society (2010), pp. 6741–6744

    Google Scholar 

  47. J. Marin, D. Vazquez, A.M. Lopez, J. Amores, B. Leibe, Random forests of local experts for pedestrian detection, in Proceedings of IEEE International Conference on Computer Vision (2013), pp. 2592–2599

    Google Scholar 

  48. P. Martins, J. Batista, Monocular head pose estimation, in Proceedings of International Conference on Image Analysis Recognition (2008), pp. 357–368

    Google Scholar 

  49. J.C. McCall, M.M. Trivedi, Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans. Intell. Transp. Syst. 7, 20–37 (2006)

    Article  Google Scholar 

  50. F. Meng-Yin, H. Yuan-Shui, A survey of traffic sign recognition, in Proceedings of International Conference on Wavelet Analysis Pattern Recognition (2010), pp. 119–124

    Google Scholar 

  51. T.P. Michalke, F. Stein, U. Franke, Towards a closer fusion of active and passive safety: optical flow-based detection of vehicle side collisions, in Proceedings of IEEE Intelligent Vehicle Symposium (2011), pp. 181–188

    Google Scholar 

  52. M. Miyaji, M. Danno, H. Kawanaka, K. Oguri, Driver’s cognitive distraction detection using AdaBoost on pattern recognition basis, in Proceedings of IEEE International Conference on Vehicular Electronics Safety (2008), pp. 51–56

    Google Scholar 

  53. M. Miyaji, H. Kawanaka, K. Oguri, Effect of pattern recognition features on detection for driver’s cognitive distraction, in Proceedings of IEEE International Conference on Intelligent Transportation Systems (2010), pp. 605–610

    Google Scholar 

  54. A. Møgelmose, M.M. Trivedi, T.B. Moeslund, Vision based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans. Intell. Transp. Syst. 13, 1484–1497 (2012)

    Article  Google Scholar 

  55. S. Morales, R. Klette, A third eye for performance evaluation in stereo sequence analysis, in Proceedings of International Conference on Computer Analysis Images Patterns. LNCS 5702 (2009), pp. 1078–1086

    Google Scholar 

  56. S. Müller-Schneiders, C. Nunn, M. Meuter, Performance evaluation of a real time traffic sign recognition system, in Proceedings of IEEE Conference on Intelligent Vehicles Symposium (2008), pp. 79–84

    Google Scholar 

  57. E. Murphy-Chutorian, M.M. Trivedi, Head pose estimation and augmented reality tracking: an integrated system and evaluation for monitoring driver awareness. IEEE Trans. Intell. Transp. Syst. 11, 300–311 (2010)

    Article  Google Scholar 

  58. W. Murray, Improving work-related road safety in New Zealand – a research report. Department of Labour, Wellington (2007)

    Google Scholar 

  59. New Zealand Ministry of Transport, Motor vehicle crash fact sheets (2010)

    Google Scholar 

  60. E. Ohn-Bar, M. Trivedi, Fast and robust object detection using visual subcategories, in Proceedings of IEEE Computer Vision Pattern Recognition Workshops (2014), pp. 179–184

    Google Scholar 

  61. R. O’Malley, M. Glavin, E. Jones, Vehicle detection at night based on tail-light detection, in Proceedings of International Symposium on Vehicular Computing Systems, vol. 2224 (2008)

    Google Scholar 

  62. M.T.R. Peiris, R.D. Jones, P.R. Davidson, P.J. Bones, Detecting behavioral microsleeps from EEG power spectra, in Proceedings of IEEE Conference on Engineering Medicine Biology Society (2006), pp. 5723–5726

    Google Scholar 

  63. M.T.R. Peiris, R.D. Jones, P.R. Davidson, G.J. Carroll, P.J. Bones, Frequent lapses of responsiveness during an extended visuomotor tracking task in non-sleep-deprived subjects. J. Sleep Res. 15, 291–300 (2006)

    Article  Google Scholar 

  64. D. Ponsa, A.M. Lopez, F. Lumbreras, J. Serrat, T. Graf, 3D vehicle sensor based on monocular vision, in Proceedings of IEEE Conference on Intelligent Transportation Systems (2005), pp. 1096–1101

    Google Scholar 

  65. D. Ponsa, A.M. Lopez, J. Serrat, F. Lumbreras, T. Graf, Multiple vehicle 3D tracking using an unscented Kalman filter, in Proceedings of IEEE Conference on Intelligent Transportation Systems (2005), pp. 1108–1113

    Google Scholar 

  66. E. Portouli, E. Bekiaris, V. Papakostopoulos, N. Maglaveras, On-road experiment for collecting driving behavioural data of sleepy drivers. Somnologie Schlafforschung Schlafmedizin 11, 259–267 (2007)

    Article  Google Scholar 

  67. L. Qiong, P. Guang-zheng, A robust skin color based face detection algorithm, in Proceedings of International Asian Conference on Informatics Control Automation Robotics (2010), pp. 525–528

    Google Scholar 

  68. M. Rezaei, R. Klette, Simultaneous analysis of driver behaviour and road condition for driver distraction detection. Int. J. Image Data Fusion 2, 217–236 (2011)

    Article  Google Scholar 

  69. M. Rezaei, R. Klette, Look at the driver, look at the road: No distraction! No accident! in Proceedings of IEEE Computer Vision Pattern Recognition (2014), pp. 129–136

    Google Scholar 

  70. H. Ryu, J. Yoon, S. Chun, S. Sull, Coarse-to-fine classification for image-based face detection, in Proceedings of International Conference on Image Video Retrieval (2006), pp. 291–299

    Google Scholar 

  71. M. Schreier, V. Willert, Robust free space detection in occupancy grid maps by methods of image analysis and dynamic B-spline contour tracking, in Proceedings of the IEEE Conference on Intelligent Transportation Systems (2012), pp. 514–521

    Google Scholar 

  72. R. Senaratne, B. Jap, S. Lal, A. Hsu, S. Halgamuge, P. Fischer, Comparing two video-based techniques for driver fatigue detection: classification versus optical flow approach. Mach. Vis. Appl. 22, 597–618 (2011)

    Article  Google Scholar 

  73. B.-S. Shin, D. Caudillo, R. Klette, Evaluation of two stereo matchers on long real-world video sequences. Pattern Recognit. 48, 113–1124 (2014)

    Google Scholar 

  74. B.-S. Shin, Z. Xu, R. Klette, Visual lane analysis and higher-order tasks: a concise review. Mach. Vis. Appl. 25, 1519–1547 (2014)

    Article  Google Scholar 

  75. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, A. Blake, Real-time human pose recognition in parts from single depth images. Stud. Comput. Intell. 411, 119–135 (2013)

    Google Scholar 

  76. M.H. Sigari, Driver hypo-vigilance detection based on eyelid behavior, in Proceedings of the International Conference on Advances Pattern Recognition (2009), pp. 426–429

    Google Scholar 

  77. S. Sivaraman, M.M. Trivedi, Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking and behavior analysis. IEEE Trans. Intell. Transp. Syst. 14, 1773–1795 (2013)

    Article  Google Scholar 

  78. S. Sivaraman, M.M. Trivedi, Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Conf. Intell. Transp. Syst. 14, 1773–1795 (2013)

    Article  Google Scholar 

  79. Synthetic Lane Data (2013), www.cvc.uab.es/adas/projects/lanemarkings/IJAT/videos.html

  80. B. Triggs, P. McLauchlan, R. Hartley, A. Fitzgibbon, Bundle adjustment – a modern synthesis, in Proceedings of the Vision Algorithms Theory Practice (2000), pp. 298–375

    Google Scholar 

  81. U.S. Department of Transportation, National Highway Traffic Safety Administration. The impact of driver inattention on near-crash/crash risk. DOT HS 810 594 (2006)

    Google Scholar 

  82. M. Vargas, J.M. Milla, S.L. Toral, F. Barrero, An enhanced background estimation algorithm for vehicle detection in urban traffic scenes. IEEE Trans. Veh. Technol. 59, 3694–3709 (2010)

    Article  Google Scholar 

  83. Virginia Tech Transportation Institute, 100-car naturalistic driving study fact sheet (2005)

    Google Scholar 

  84. H. Wang, L.B. Zhou, Y. Ying, A novel approach for real time eye state detection in fatigue awareness system, in Proceedings of Robotics Automation Mechatronics (2010), pp. 528–532

    Google Scholar 

  85. R. Wang, L. Guo, B. Tong, L. Jin, Monitoring mouth movement for driver fatigue or distraction with one camera, in Proceedings of IEEE International Conference on Intelligent Transportation Systems (2004), pp. 314–319

    Google Scholar 

  86. X. Wang, M. Yang, S. Zhou, Y. Lin, Regionlets for generic object detection, in Proceedings of the IEEE Confernce on Computer Vision (2013), pp. 17–24

    Google Scholar 

  87. A. Wedel, H. Badino, C. Rabe, H. Loose, U. Franke, D. Cremers, B-spline modeling of road surfaces with an application to free-space estimation. IEEE Trans. Intell. Transp. Syst. 10, 572–583 (2009)

    Article  Google Scholar 

  88. A. Wedel, U. Franke, H. Badino, D. Cremers, B-spline modeling of road surfaces for freespace estimation, in Proceedings of the IEEE Intelligent Vehicles Symposium (2008), pp. 828–833

    Google Scholar 

  89. W. Wen, C. Xilin, Y. Lei, Detection of text on road signs from video. IEEE Trans. Intell. Transp. Syst. 6, 378–390 (2005)

    Article  Google Scholar 

  90. W.W. Wierwille, L.A. Ellsworth, Evaluation of driver drowsiness by trained raters. Accid. Anal. Prev. 26, 571–581 (1994)

    Article  Google Scholar 

  91. W.S. Wijesoma, K.R.S. Kodagoda, A.P. Balasuriya, Road-boundary detection and tracking using ladar sensing. IEEE Trans. Robot. Autom. 20, 456–464 (2004)

    Article  Google Scholar 

  92. P.I. Wilson, J. Fernandez, Facial feature detection using Haar classifiers. J. Comput. Sci. Coll. 21, 127–133 (2006)

    Google Scholar 

  93. YALE Face Database (2013), vision.ucsd.edu/~iskwak/ExtYaleDatabase/Yale20Face20Database.htm

    Google Scholar 

  94. M.H. Yang, D. Kriegman, N. Ahuja, Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24, 34–58 (2002)

    Google Scholar 

  95. J.J. Yebes, L.M. Bergasa, R. Arroyo, A. Lazaro, Supervised learning and evaluation of KITTI’s cars detector with DPM, in Proceedings of the IEEE Intelligent Vehicle Symposium (2014), pp. 768–773

    Google Scholar 

  96. C. Zhang, Z. Zhang, A survey of recent advances in face detection. Microsoft Research. Technical Report MSR-TR-2010-66 (2010)

    Google Scholar 

  97. Z. Zhang, Y. Shan, Incremental motion estimation through local bundle adjustment Microsoft Research. Technical report MSR-TR-01-54 (2001)

    Google Scholar 

  98. N. Zhiheng, S. Shiguang, Y. Shengye, C. Xilin, G. Wen, 2D cascaded AdaBoost for eye localization, in Proceedings of the International Conference on Pattern Recognition (2006), pp. 1216–1219

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Qazvin Islamic Azad University, Qazvin, Iran

    Mahdi Rezaei

  2. Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, New Zealand

    Reinhard Klette

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  1. Mahdi Rezaei
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Rezaei, M., Klette, R. (2017). Driver-Environment Understanding. In: Computer Vision for Driver Assistance. Computational Imaging and Vision, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-50551-0_2

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