Skip to main content
SpringerLink
Log in

Vehicle Detection and Distance Estimation

  • Chapter
  • First Online:
Computer Vision for Driver Assistance

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

Abstract

“Collision warning systems” are actively researched in the area of computer vision and the automotive industry. Using monocular vision only, this chapter discusses the part of our study that aims at detecting and tracking the vehicles ahead, to identify safety distances, and to provide timely information to assist a distracted driver under various weather and lighting conditions. As part of the work presented in this chapter, we also adopt the previously discussed dynamic global Haar (DGHaar) features for vehicle detection. We introduce “taillight segmentation” and a “virtual symmetry detection” technique for pairing the rear-light contours of the vehicles on the road. Applying a heuristic geometric solution, we also develop a method for inter-vehicle “distance estimation” using only a monocular vision sensor. Inspired by Dempster–Shafer theory, we finally fuse all the available clues and information to reach a higher degree of certainty. The proposed algorithm is able to detect vehicles ahead both at day and night, and also for a wide range of distances. Experimental results under various conditions, including sunny, rainy, foggy, or snowy weather, show that the proposed algorithm outperforms other currently published algorithms that are selected for comparison.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    We refer to challenging lighting conditions when the subject (e.g. a vehicle) is located under any non-ideal condition such as very low light, night, very bright reflections, rainy, foggy, or snowy weather, where object detection becomes very difficult, from a technical point of view.

  2. 2.

    Experimentally identified as the optimum value.

  3. 3.

    Instead of 10, it could be any other number. The more the sets the better the interpolation results. The number 10 proved to be sufficient for obtaining an acceptable interpolation.

Bibliography

  1. A. Alahi, R. Ortiz, P. Vandergheynst, FREAK: fast retina keypoint, in Proceedings of the IEEE Computer Vision Pattern Recognition (2012), pp. 510–517

    Google Scholar 

  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. K. Briechle, U.D. Hanebeck, Template matching using fast normalized cross correlation, in Proceedings of the Aerospace Defense Sensing Simulation Controls (2001), pp. 95–102

    Google Scholar 

  4. J. Choi, Realtime on-road vehicle detection with optical flows and Haar-like feature detectors. University of Illinois at Urbana-Champaign (2007)

    Google Scholar 

  5. R.O. Duda, P.E. Hart, Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)

    Article  MATH  Google Scholar 

  6. EPFL University, Computer Vision Laboratory: EPFL multi-view car dataset (2012), cvlab.epfl.ch/data/pose

    Google Scholar 

  7. P.F. Felzenszwalb, R.B. Girshick, D. McAllester, Cascade object detection with deformable part models, in Proceedings of the IEEE Computer Vision Pattern Recognition (2010), pp. 2241–2248

    Google Scholar 

  8. P.F. Felzenszwalb, R.B. 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 

  9. H. Freeman, On the encoding of arbitrary geometric configurations. IRE Trans. Electron. Comput. 10, 260–268 (1961)

    Article  MathSciNet  Google Scholar 

  10. F. Garcia, P. Cerri, A. Broggi, A. De la Escalera, J.M. Armingol, Data fusion for overtaking vehicle detection based on radar and optical flow, in Proceedings of the IEEE Intelligent Vehicles Symposium (2012), pp. 494–499

    Google Scholar 

  11. H.Z. Gu, S.Y. Lee, Car model recognition by utilizing symmetric property to overcome severe pose variation. Mach. Vis. Appl. 24, 255–274 (2013)

    Article  Google Scholar 

  12. S. Han, Y. Han, H. Hahn, Vehicle detection method using Haar-like feature on real time system. World Acad. Sci. Eng. Technol. (2009), pp. 455–459

    Google Scholar 

  13. A. Haselhoff, A. Kummert, A vehicle detection system based on Haar and triangle features, in Proceedings of the IEEE Intelligent Vehicles Symposium (2009), pp. 261–266

    Google Scholar 

  14. A. Jazayeri, C. Hongyuan, Z. Jiang Yu, M. Tuceryan, Vehicle detection and tracking in car video based on motion model. IEEE Trans. Intell. Transp. Syst. 12, 583–595 (2011)

    Article  Google Scholar 

  15. R. Jiang, R. Klette, T. Vaudrey, S. Wang, New lane model and distance transform for lane detection and tracking, in Proceedings of the International Conference on Computer Analysis Images Patterns. LNCS 5702 (2009), pp. 1044–1052

    Google Scholar 

  16. N. Kiryati, Y. Eldar, A.M. Bruckstein, A probabilistic hough transform. Pattern Recognit. 24, 303–316 (1991)

    Article  MathSciNet  Google Scholar 

  17. KITTI Benchmark website: car detection benchmark (2013), www.cvlibs.net/datasets/kitti/eval_object.php

    Google Scholar 

  18. 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 

  19. D. Koks, S. Challa, An introduction to Bayesian and Dempster–Shafer data fusion. Technical report, TR, DSTO-TR-1436, DSTO Systems Sciences Laboratory (2005)

    Google Scholar 

  20. H. Lili, M. Barth, Tightly-coupled LIDAR and computer vision integration for vehicle detection, in Proceedings of Intelligent Vehicles Symposium (2009), pp. 604–609

    Google Scholar 

  21. Y.K. Liu, B. Žalik, An efficient chain code with Huffman coding. Pattern Recognit. 38, 553–557 (2005)

    Article  Google Scholar 

  22. J. Matas, C. Galambos, J. Kittler, Robust detection of lines using the progressive probabilistic Hough transform. Comput. Vis. Image Underst. 78, 119–137 (2000)

    Article  Google Scholar 

  23. National Highway Traffic Safety Administration, Traffic safety facts, U.S. Department of Transportation (2013)

    Google Scholar 

  24. R. O’Malley, E. Jones, M. Glavin, Rear-lamp vehicle detection and tracking in low-exposure color video for night conditions. IEEE Trans. Intell. Transp. Syst. 11, 453–462 (2010)

    Article  Google Scholar 

  25. C. Premebida, G. Monteiro, U. Nunes, P. Peixoto, A Lidar and vision-based approach for pedestrian and vehicle detection and tracking, in Proceedings of IEEE Conference on Intelligent Transportation Systems Conference (2007), pp. 1044–1049

    Google Scholar 

  26. M. Rezaei, R. Klette, Adaptive Haar-like classifier for eye status detection under non-ideal lighting conditions, in Proceedings of International Conference on Image Vision Computing New Zealand (ACM, 2012), pp. 521–526

    Google Scholar 

  27. M. Rezaei, M. Terauchi, iROADS dataset (Intercity Roads and Adverse Driving Scenarios), EISATS, Set 10 (2014), ccv.wordpress.fos.auckland.ac.nz/eisats/set-10/

    Google Scholar 

  28. M. Rezaei, M. Terauchi, R. Klette, Monocular vehicle detection and distance estimation under challenging lighting conditions. IEEE Trans. Intell. Transp. Syst. 16, 2723–2743 (2014)

    Article  Google Scholar 

  29. M. Rezaei, H. Ziaei Nafchi, S. Morales, Global Haar-like features: a new extension of classic Haar features for efficient face detection in noisy images, in Proceedings of Pacific-Rim Symposium on Image Video Technology. LNCS 8333 (2013), pp. 302–313

    Google Scholar 

  30. E. Rosten, T. Drummond, Machine learning for high-speed corner detection, in Proceedings of European Conference on Computer Vision (2006), pp. 430–443

    Google Scholar 

  31. E. Rublee, V. Rabaud, K. Konolige, G. Bradski, ORB: an efficient alternative to SIFT or SURF, in Proceedings of IEEE International Conference on Computer Vision (2011), pp. 2564–2571

    Google Scholar 

  32. D. Santos, P.L. Correia, Car recognition based on back lights and rear view features, in Proceedings of the Image Analysis Multimedia Interactive Services (2009), pp. 137–140

    Google Scholar 

  33. G. Shafer, A Mathematical Theory of Evidence (Princeton University Press, Princeton, 1976)

    MATH  Google Scholar 

  34. J. Shi, C. Tomasi, Good features to track, in Proceedings of the IEEE Computer Vision Pattern Recognition (1994), pp. 593–600

    Google Scholar 

  35. G. Toulminet, M. Bertozzi, S. Mousset, A. Bensrhair, A. Broggi, Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis. IEEE Trans. Image Process. 15, 2364–2375 (2006)

    Article  Google Scholar 

  36. S. Tuohy, D. O’Cualain, E. Jones, M. Glavin, Distance determination for an automobile environment using inverse perspective mapping in OpenCV, in Proceedings of the Signals and Systems Conference (2010), pp. 100–105

    Google Scholar 

  37. 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 

  38. N. Vinh Dinh, N. Thuy Tuong, N. Dung Duc, J. Jae Wook, Toward real-time vehicle detection using stereo vision and an evolutionary algorithm, in Proceedings of the Vehicular Technology Conference (2012), pp. 1–5

    Google Scholar 

  39. K. ZuWhan, Robust lane detection and tracking in challenging scenarios. IEEE Trans. Intell. Transp. Syst. 9, 16–26 (2008)

    Article  Google Scholar 

Download references

Author information

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

Authors
  1. Mahdi Rezaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reinhard Klette
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Rezaei, M., Klette, R. (2017). Vehicle Detection and Distance Estimation. In: Computer Vision for Driver Assistance. Computational Imaging and Vision, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-50551-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50551-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50549-7

  • Online ISBN: 978-3-319-50551-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation