Monovision-based vehicle detection, distance and relative speed measurement in urban traffic

Manuel Ibarra Arenado; Juan Maria Pérez Oria; Carlos Torre-Ferrero; Luciano Alonso Rentería

Monovision-based vehicle detection, distance and relative speed measurement in urban traffic

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Author(s): Manuel Ibarra Arenado ¹ ; Juan Maria Pérez Oria ¹ ; Carlos Torre-Ferrero ¹ ; Luciano Alonso Rentería ¹
- Affiliations: 1: Control Engineering Group, Department of Electronic Technology and Systems Engineering, University of Cantabria, Santander, Spain
Source: Volume 8, Issue 8, December 2014, p. 655 – 664
DOI: 10.1049/iet-its.2013.0098 , Print ISSN 1751-956X, Online ISSN 1751-9578

Received 24/05/2013, Accepted 09/01/2014, Revised 27/12/2013, Published 25/02/2014

This study presents a monovision-based system for on-road vehicle detection and computation of distance and relative speed in urban traffic. Many works have dealt with monovision vehicle detection, but only a few of them provide the distance to the vehicle which is essential for the control of an intelligent transportation system. The system proposed integrates a single camera reducing the monetary cost of stereovision and RADAR-based technologies. The algorithm is divided in three major stages. For vehicle detection, the authors use a combination of two features: the shadow underneath the vehicle and horizontal edges. They propose a new method for shadow thresholding based on the grey-scale histogram assessment of a region of interest on the road. In the second and third stages, the vehicle hypothesis verification and the distance are obtained by means of its number plate whose dimensions and shape are standardised in each country. The analysis of consecutive frames is employed to calculate the relative speed of the vehicle detected. Experimental results showed excellent performance in both vehicle and number plate detections and in the distance measurement, in terms of accuracy and robustness in complex traffic scenarios and under different lighting conditions.

References

1. 1)
  - 18. Álvarez, S., Sotelo, M.Á., Ocaña, M., Llorca, D.F., Parra, I., Bergasa, L.M.: ‘Perception advances in outdoor vehicle detection for automatic cruise control’, Robotica, 2010, 28, pp. 765–779 (doi: 10.1017/S0263574709990464).
2. 2)
  - A. Broggi , M. Bertozzi , A. Fascioli , C.G..L. Bianco , A. Piazzi . Visual perception of obstacles and vehicles for platooning. IEEE Trans. Intell. Transp. Syst. , 3 , 164 - 176
3. 3)
  - 34. Chengwen, H., Yannan, Z., Jiaxin, W., Zehong, Y.: ‘An improved method for the character recognition based on SVM’. Proc. of the IASTED Int. Conf. on Artificial Intell. and Applications, 2006, pp. 457–461.
4. 4)
  - 35. Martín, F., Garcia, M., Alba, J.L.: ‘New methods for automatic reading of VLP's’. Proc. of the IASTED Int. Conf. on Signal Processing, Pattern Recognition and Applications, SPPRA, 2002.
5. 5)
  - 26. Tzomakas, C., Seelen, W.: ‘Vehicle detection in traffic scenes using shadows’ Tech. Rep. 98-06, (Institut FurNeuroinformatik, Ruht-universitat, Bochum, Germany, 1998).
6. 6)
  - 32. Suryanarayana, P.V., Mitra, S.K., Banerjee, A., Roy, A.K.: ‘A morphology based approach for car license plate extraction’. IEEE Indicon Conf., Chennai, India, 2005, pp. 24–27.
7. 7)
  - B. Leibe , K. Schindler , N. Cornelis , L. Van Gool . Coupled object detection and tracking from static cameras and moving vehicles. IEEE Trans. Pattern Anal. Mach. Intell. , 10 , 1683 - 1698
8. 8)
  - 19. Kuehnle, A.: ‘Symmetry-based recognition for vehicle rears’, Pattern Recogn. Lett., 1991, 12, pp. 249–258 (doi: 10.1016/0167-8655(91)90039-O).
9. 9)
  - 38. Otsu, N.: ‘A threshold selection method for gray level histograms’, IEEE Trans. Syst. Man Cybern., 1979, pp. 62–66.
10. 10)
  - 29. Yanamura, Y., Goto, M., Nishiyama, D., Soga, M., Nakatani, H., Saji, H.: ‘Extraction and tracking of the license plate using Hough transform and voted block matching’. Proc. IEEE Intelligent Vehicles Symp., 2003, pp. 243–246.
11. 11)
  - 33. Kim, K.K., Kim, K.I., Kim, J.B., Kim, H.J.: ‘Learning-based approach for license plate recognition’, IEEE Neural Network for Signal Processing Workshop 2000, Sydney, Australia, 2000, pp. 614–623.
12. 12)
  - 30. Li, W.J., Liang, D.Q., Zhang, Q., Fan, X.: ‘A novel approach for vehicle license plate location based on edge-color pair’, Chin. J. Comput., 2004, 27, (2), pp. 204–208.
13. 13)
  - 37. Hung, K.M., Hsieh, C.T.: ‘A real-time mobile vehicle license plate detection and recognition’, J. Sci. Eng., 2010, 13, pp. 433–442.
14. 14)
  - 22. Paroli, P., Piccioli, G.: ‘A feature-based recognition scheme for traffic scenes’. Proc. Intelligent Vehicle Symp., Detroit, 1995, pp. 229–234..
15. 15)
  - 15. Michels, J., Saxena, A., Ng, A.Y.: ‘High speed obstacle avoidance using monocular vision and reinforcement learning’. 22nd Int. Conf. on Machine Learning, Bonn, Germany, 2005, pp. 593–600.
16. 16)
  - 9. Makris, A., Perrollaz, M., Paromtchik, I.E., Laugier, C.: ‘Integration of visual and depth information for vehicle detection’. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, San Francisco, USA, 2011.
17. 17)
  - 10. Amditis, A., Bertolazzi, E., Bimpas, M., et al: ‘A holistic approach to the integration of safety applications: the INSAFES subproject within the European framework programme 6 integrating project PReVENT’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (3), pp. 554–566 (doi: 10.1109/TITS.2009.2036736).
18. 18)
  - 13. Leibe, Bastian, Schindler, Konrad, Cornelis, Nico, Van Gool, Lue: ‘Stereo vision-based vehicle detection’. Proc. IEEE Intelligent Vehicles Symp., 2000, pp. 39–44.
19. 19)
  - 39. Beauchemin, S.S., Bauer, M.A., Kowsari, T., Ji, C.: ‘Portable and scalable vision-based vehicular instrumentation for the analysis of driver intentionality’, IEEE Trans. Instrum. Meas., 2011, 61, (2), pp. 391–401 (doi: 10.1109/TIM.2011.2164854).
20. 20)
  - 6. Nedevschi, S., Danescu, R., Frentiu, D., et al: ‘High accuracy stereovision approach for obstacle detection on non-planar roads’. Proc. IEEE Intelligent Engineering Systems (INES), Cluj Napoca, Romania, 2004, pp. 211–216.
21. 21)
  - 3. Skutek, M., Mekhaiel, M., Wanielik, G.: ‘A PreCrash system based on radar for automotive applications’. Proc. IEEE of Intelligent Vehicles Symp., Columbus, 2003, pp. 37–41.
22. 22)
  - 1. Bertolazzi, E., Biral, F., Da Lio, M., Saroldi, A., Tango, F.: ‘Supporting drivers in keeping safe speed and safe distance’, IEEE Trans. Intell. Trans. Syst., 2010, 11, (3), pp. 525–538 (doi: 10.1109/TITS.2009.2035925).
23. 23)
  - 20. Kalinke, T., Tzomakas, C., von Seelen, W.: ‘A texture-based object detection and an adaptive model-based classification’. Proc. IEEE Int. Conf. on Intelligent Vehicles, Sttutgard, 1998, pp. 143–148.
24. 24)
  - 27. Van Leeuwen, M.B., Groen, F.C.A.: ‘Vehicle detection with a mobile camera, spotting midrange, distant and passing cars’, IEEE Robot. Autom. Mag., 2005, 12, (1), pp. 37–43 (doi: 10.1109/MRA.2005.1411417).
25. 25)
  - 12. Petrovic, V., Cootes, T.: ‘Analysis of features for rigid structure vehicle type recognition’. Proc. British Machine Vision Conf., 2004, pp. 587–596B.
26. 26)
  - 24. Michalke, T., Stein, F., Franke, U.: ‘Towards a closer fusion of active and passive safety: optical flow-based detection of vehicle side collisions’. Proc. Intel. Vehicles Symp. (IV), Stuttgart, 2011, pp. 181–188.
27. 27)
  - 4. Velupillai, S., Guvenc, L.: ‘Laser scanners for driver-assistance systems in intelligent vehicles’, IEEE Control Syst. Mag., 2009, 29, (2), pp. 17–19 (doi: 10.1109/MSP.2008.931716).
28. 28)
  - 25. Mori, H., Charkai, N.: ‘Shadow and rhythm as sign patterns of obstacle detection’. Proc. Int. Symp. on Industrial Electronics, Budapest, 1993, pp. 271–277.
29. 29)
  - 17. Nieto, M., Arróspide, J., Stavens, D., Salgado, L.: ‘Road environment modelling using robust perspective analysis and recursive Bayesian segmentation’, Mach. Vis. Appl., 2011, 22, pp. 927–945 (doi: 10.1007/s00138-010-0287-7).
30. 30)
  - 21. Guo, D., Fraichard, T., Xie, M., Laugier, C.: ‘Color modelling by spherical influence field in sensing driving environment’. Proc. IEEE Intelligent Vehicle Symp., Dearborn, 2000, pp. 249–254.
31. 31)
  - 2. Toulminet, G., Bertozzi, M., Mousset, S., Bemsrhair, A., Broggi, A.: ‘Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis’, IEEE Trans. Intell. Trans. Syst., 2006, 15, (8), pp. 2364–2375.
32. 32)
  - 11. Michalke, T., Fritsch, J., Goerick, C.: ‘A biologically-inspired vision architecture for resource-constrained intelligent vehicles’, Comput. Vis. Image Underst., 2010, 114, (5), pp. 548–563 (doi: 10.1016/j.cviu.2009.12.007).
33. 33)
  - 31. Liu, G.Q., Zhang, X.S., Zhang, X.B.: ‘License plate location based on texture characteristic of image’, J. Image Graph., 2005, 10, (11), pp. 1420–1423.
34. 34)
  - 23. Bertozzi, M., Broggi, A., Castelluccio, S.: ‘A real-time oriented system for vehicle detection’, J. Syst. Archit., 1997, pp. 317–325 (doi: 10.1016/S1383-7621(96)00106-3).
35. 35)
  - Z. Sun , G. Bebis , R. Miller . On-road vehicle detection: a review. IEEE Trans. Pattern Anal. Mach. Intell. , 5 , 694 - 711
36. 36)
  - 28. Yang, W.P., Li, J.C., Shen, Z.K.: ‘Technique of automatic orientation of car license plate targets’, J. Image Graph., 2002, 7, pp. 835–839.
37. 37)
  - 16. Gat, I., Benady, M., Shashua, A.: ‘A monocular vision advance warning system for the automotive aftermarket’. SAE Technical Paper, 2005.
38. 38)
  - 36. Li, W., Zhu, Z., Jiang, Z.: ‘A new method for license plate location based on Top-Hat transform and wavelet transform’. Int. Conf. in Innovative Computing, Inform. and Control, Taiwan, 2009, pp. 958–961.
39. 39)
  - 5. Alonso, L., Oria, J.P., Fernández, M., et al: ‘Genetically tuned controller of an adaptive cruise control for urban traffic based on ultrasounds’, Lect. Notes Comput. Sci., 2010, 6553, pp. 479–485 (doi: 10.1007/978-3-642-15822-3_58).

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Monovision-based vehicle detection, distance and relative speed measurement in urban traffic

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