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Dynamic obstacle identification based on global and local features for a driver assistance system

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Abstract

This paper proposes a novel dynamic obstacle recognition system combining global feature with local feature to identify vehicles, pedestrians and unknown backgrounds for a driver assistance system. The proposed system consists of two main procedures: a dynamic obstacle detection model to localize an area containing a moving obstacle, and an obstacle identification model, which is a hybrid of global and local information, for recognizing an obstacle with and without occlusion. A dynamic saliency map is used for localizing an area containing a moving obstacle. For the global feature analysis, we propose a modified GIST using orientation features with MAX pooling, which is robust to translation and size variations of an object. Although the global features are a compact way to represent an object and provide a good accuracy for non-occluded objects, they are sensitive to image translation and occlusion. Thus, a local feature-based identification model is also proposed and combined with the global feature. As such, for the obstacle identification problem, the proposed system mainly follows the global feature-based object identification. If the global feature-based model identifies a candidate area as background, the system verifies the area again using the local feature-based model. As a result, the proposed system is able to provide information on both the appearance of obstacles and the class of an obstacle. Experimental results show that the proposed model can successfully detect obstacle candidates and robustly identify obstacles with and without occlusion.

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References

  1. Fleming B (2008) Automotive safety and convenience electronics. J IEEE Veh Tech 3:10–40

    Google Scholar 

  2. Kuehnle A (2002) Symmetry-based recognition for vehicle rears. Patt Recog Lett 90(7):1258–1271

    Google Scholar 

  3. Buluswar S, Draper B (1998) Color machine vision for autonomous vehicles. Int J Eng Appl Artif Intell 1(2):245–256

    Article  Google Scholar 

  4. Dickmanns E et al (1994) The seeing passenger car ‘vamors-p’. Proc Int Symp Int Veh 24–26

  5. Goerick C, Detlev N, Wemer M (1996) Artificial neural networks in real-time car detection and tracking applications. J Patt Recog Lett 17:335–343

    Article  Google Scholar 

  6. Kalinke T, Tzomakas C, Seelen W (1998) A texture-based object detection and an adaptive model-based classification. Proc IEEE Int Conf Intell Veh 143–148

  7. Lisin D, Matter M, Blaschko M (2008) Combining local and global image features for object class recognition. CVPR

  8. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86

    Article  Google Scholar 

  9. Oliva A (2005) Gist of the scene. J Neurobiol Atten CA 251–256

    Google Scholar 

  10. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. Proc Compt Vis Pattern Recog 511–518

  11. Leibe B, Leonardis A, Schiele B (2004) Combined object categorization and segmentation with an implicit shape model. Proc Euro Conf Compt Vis Prague

  12. Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in cortex. J Neurosci 2:1019–1025

    Google Scholar 

  13. Ban S, Lee I, Lee M (2008) Dynamic visual selective attention. J Neurocompt 71(4–6):853–856

    Article  Google Scholar 

  14. Woo J, Lim Y, Lee M (2009) Obstacle categorization based on hybridizing global and local features. LNCS 5864:1–10

    Google Scholar 

  15. Kadir T, Brady M (2001) Scale, saliency and image description. Int J Compt Vis 45(2):83–105

    Article  MATH  Google Scholar 

  16. Choi S, Jung B, Ban S, Niitsuma H, Lee M (2006) Biologically motivated vergence control system using human-like selective attention model. J Neurocompt 69(4–6):537–558

    Google Scholar 

  17. Treisman A, Gelde G (1980) A feature-integration theory of attention. J Cog Psychol 12(1):97–136

    Article  Google Scholar 

  18. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. J IEEE Trans Patt Anal Mach Intell 20(11):1254–1259

    Article  Google Scholar 

  19. Park S, An K, Lee M (2002) Saliency map model with adaptive masking based on independent component analysis. J Neurocompt 49(1):417–422

    Article  Google Scholar 

  20. Jeong S, Ban S, Lee M (2008) Stereo saliency map considering affective factors and selective motion analysis in a dynamic environment. J Neural Netw 21(10):1420–1430

    Article  Google Scholar 

  21. Huber D, Wiesel T (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 148:574–591

    Google Scholar 

  22. Fukushima K (2005) Use of non-uniform spatial blur for image comparison: symmetry axis extraction. J Neural Netw 18:23–32

    Article  MATH  Google Scholar 

  23. Burges C (1998) A tutorial on support vector machines for pattern recognition. J Data Min Knowl Discov 2:121–167

    Article  Google Scholar 

  24. Harris C, Stephens M (1988) A combined corner and edge detector. Proc 4th Alvey Vision Conf 147–151

  25. Rodgers J, Nicewander W (1988) Thirteen ways to look at the correlation coefficient. Am J Stat 42(1):59–66

    Article  Google Scholar 

  26. Computational Vision Lab., Caltech., http://www.vision.caltech.edu

  27. Center for Biological & Computational Learning, MIT, http://cbcl.mit.edu

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Acknowledgments

This research was supported by the Daegu Gyeongbuk Institute of Science and Technology (DGIST) and the IT R&D program of MKE/KETI. [10033776, Core technology development of large-scale, intelligent and cooperative surveillance system]. Also, this research was supported by the Converging Research Center Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2009-0082262)(50%).

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu, 702-701, Korea

    Jeong-Woo Woo & Minho Lee

  2. Division of Advanced Industrial Science and Technology, Daegu Gyeongbuk Institute of Science and Technology, 711 Hosan-Dong, Dalseo-Gu, Taegu, 704-230, Korea

    Young-Chul Lim

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  1. Jeong-Woo Woo
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Correspondence to Minho Lee.

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Woo, JW., Lim, YC. & Lee, M. Dynamic obstacle identification based on global and local features for a driver assistance system. Neural Comput & Applic 20, 925–933 (2011). https://doi.org/10.1007/s00521-010-0401-9

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