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Vanishing Point and Gabor Feature Based Multi-resolution On-Road Vehicle Detection

  • Hong Cheng
  • Nanning Zheng
  • Chong Sun
  • Huub van de Wetering
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

Robust and reliable vehicle detection is a challenging task under the conditions of variable size and distance, various weather and illumination, cluttered background, the relative motion between the host vehicle and background. In this paper we investigate real-time vehicle detection using machine vision for active safety in vehicle applications. The conventional search method of vehicle detection is a full search one using image pyramid,which processes the image patches in same way and costs same computing time, even for no vehicle region according to prior knowledge.

Our vehicle detection approach includes two basic phases. In the hypothesis generation phase, we determine the Regions of Interest (ROI) in an image according to lane vanishing points; furthermore, near, middle, and far ROIs, each with a different resolution, are extracted from the image. From the analysis of horizontal and vertical edges in the image, vehicle hypothesis lists are generated for each ROI. Finally, a hypothesis list for the whole image is obtained by combining these three lists. In the hypothesis validation phase, we propose a vehicle validation approach using Support Vector Machine (SVM) and Gabor feature. The experimental results show that the average right detection rate reach 90% and the average execution time is 30ms using a Pentium(R)4 CPU 2.4GHz.

Keywords

Support Vector Machine Image Patch Vertical Edge Adaptive Cruise Control Gabor Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Zheng, N.N., Tang, S., Cheng, H., Li, Q., Lai, G., Wang, F.Y.: Toward Intelligent Driver-Assistance and Safety Warning System. IEEE Intelligent Systems 19(2), 8–11 (2004)CrossRefGoogle Scholar
  2. 2.
    Broggi, A., Cerri, P., Antonello, P.C.: Multi-Resolution Vehicle Detection Using Artificial Vision. In: IEEE International Symposium on Intelligent Vehicle., pp. 310–314 (2004)Google Scholar
  3. 3.
    Du, Y., Papanikolopoulos, N.P.: Real-Time Vehicle Following Through a Novel Symmetry-Based Approach. In: IEEE International Conference on Robotics and Automation, vol. 4, pp. 3160–3165 (1997)Google Scholar
  4. 4.
    Rasmussen, C.: Grouping Dominant Orientations for Ill-Structured Road Following. In: Proceedings of the 2004 IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 470–477 (2004)Google Scholar
  5. 5.
    Manjunath, B., Ma, W.: Texture Features for Browsing and Retrieval of Image Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8), 837–842 (1996)CrossRefGoogle Scholar
  6. 6.
    Sun, Z.H., Miller, R., Bebis, G., DiMeo, D.: On-road Vehicle Detection Using Evolutionary Gabor Filter Optimization. IEEE Transactions on Intelligent Transportation Systems 6(2), 125–137 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hong Cheng
    • 1
  • Nanning Zheng
    • 1
  • Chong Sun
    • 1
  • Huub van de Wetering
    • 1
    • 2
  1. 1.Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityChina
  2. 2.Technische Universiteit EindhovenEindhovenThe Netherlands

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