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Asian Conference on Computer Vision

ACCV 2014: Computer Vision - ACCV 2014 Workshops pp 383–398Cite as

Scene Parsing and Fusion-Based Continuous Traversable Region Formation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9008))

Abstract

Determining the categories of different parts of a scene and generating a continuous traversable region map in the physical coordinate system are crucial for autonomous vehicle navigation. This paper presents our efforts in these two aspects for an autonomous vehicle operating in open terrain environment. Driven by the ideas that have been proposed in our Cognitive Architecture, we have designed novel strategies for the top-down facilitation process to explicitly interpret spatial relationship between objects in the scene, and have incorporated a visual attention mechanism into the image-based scene parsing module. The scene parsing module is able to process images fast enough for real-time vehicle navigation applications. To alleviate the challenges in using sparse 3D occupancy grids for path planning, we are proposing an approach to interpolate the category of occupancy grids not hit by 3D LIDAR, with reference to the aligned image-based scene parsing result, so that a continuous \(2\frac{1}{2}D\) traversable region map can be formed.

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References

  1. Goodale, M.A., Milner, A.D.: Separate visual pathways for perception and action. trends Neurosci. 15(1), 20–25 (1992)

    Article  Google Scholar 

  2. Ng, G.W.: Brain-Mind Machinery. World Scientific, London (2009)

    Book  Google Scholar 

  3. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, pp. 1150–1157 (1999)

    Google Scholar 

  4. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)

    Google Scholar 

  5. Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in Cortex. Nature Neurosci. 2, 1019–1025 (1999)

    Article  Google Scholar 

  6. Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 411–426 (2007)

    Article  Google Scholar 

  7. Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained multiscale deformable part model. In: CVPR (2008)

    Google Scholar 

  8. Viola, P., Michael J.J.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001)

    Google Scholar 

  9. Felzenszwalb, P., Girshick, R. McAllester, D.: Cascade object detection with deformable part models. In: CVPR (2010)

    Google Scholar 

  10. Laxebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)

    Google Scholar 

  11. Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. Comput. Vis. 42(3), 145–175 (2001)

    Article  MATH  Google Scholar 

  12. Torralba, A., Murphy, K., P., Freeman, W.T., Rubin, M. A.: Context-based vision system for place and object recognition. In: ICCV, pp. 1023–1029 (2003)

    Google Scholar 

  13. Siagian, C., Itti, L.: Rapid biologically-inspired scene classication using features shared with visual attention. PAMI 29(2), 300–312 (2007)

    Article  Google Scholar 

  14. Renniger, L., Malik, J.: When is scene identification just texture recognition? Vis. Res. 44, 2301–2311 (2004)

    Article  Google Scholar 

  15. Tighe, J., Lazebnik, S.: SuperParsing: scalable nonparametric image parsing with superpixels. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 352–365. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  16. Li, L.J., Socher, R., Li, F.F.: Towards total scene understanding: classification, annotation and segmentation in an automatic framework. In: CVPR (2009)

    Google Scholar 

  17. Du, L., Ren, L., Dunson, D., B., Carin, L.: A Bayesian model for simultaneous image clustering, annotation and object segmentation. In: NIPS (2009)

    Google Scholar 

  18. Rabinovich, A., Vedaldi, A., Galleguillos, C.: Object in context. In: ICCV (2007)

    Google Scholar 

  19. Galleguillos, C., Belongie, S.: Context-based object categorization: a critical survey. J. Comput. Vis. Image Underst. 114(6), 712–722 (2010)

    Article  Google Scholar 

  20. He, X., Zemel, R., Carreira-Perpindn, M.A.: Multiscale conditional random fields for image labelling. In: CVPR, pp. 695–702 (2004)

    Google Scholar 

  21. Kumar, S., Hebert, M.: A hierarchical field framework for unified context-based classification. In: ICCV, pp. 1284–1291 (2005)

    Google Scholar 

  22. Verbeek, J., Triggs, B.: Scene segmentation with conditional random fields learned from partially labeled images. In: NIPS (2008)

    Google Scholar 

  23. http://en.wikipedia.org/wiki/DARPA_Grand_Challenge

  24. Vandapel, N., Huber, D.F., Kapuria, A., Hebert, M.: Natural terrain classification using three-dimensional Ladar data for ground robot mobility. J. Field Robot. 23(10), 839–861 (2006)

    Article  Google Scholar 

  25. Himmelsbach, M., Luettel, T., Wuensche, H.J.: Real-time object classification in 3D point clouds using point feature histograms. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, USA (2009)

    Google Scholar 

  26. Thrun, S., et al.: Stanley: the robot that won the DARPA grand challenge. J. Robot. Syst. 23(9), 661–692 (2006)

    Google Scholar 

  27. Rasmussen, C.: A hybrid vision+Ladar rural road follower. In: Proceedings of the IEEE Conference on Robotics and Automation, pp. 156–161 (2006)

    Google Scholar 

  28. Manz, M., Himmelsbach, M., Luettel, T., Wuensche, H.: Detection and tracking of road networks in rural terrain by fusing vision and LIDAR. In: Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4562–4568 (2011)

    Google Scholar 

  29. Ng, G.W., Xiao, X., Chan, R.Z., Tan, Y.S.: Scene understanding using DSO cognitive architecture. In: Proceedings of the 15th International Conference on Information Fusion (2012)

    Google Scholar 

  30. Zhao, G., Xiao, X., Yuan, J., Ng, G.W.: Fusion of 3D-LIDAR and camera data for scene parsing. J. Vis. Commun. Image Represent. 25(1), 165–183 (2013)

    Article  Google Scholar 

  31. Hochstein, S., Ahissar, M.: View from the top: hierarchies and reverse hierarchies in the visual system. Neuron 36, 791–804 (2002)

    Article  Google Scholar 

  32. Bar, M.: A cortical mechanism for triggering top-down facilitation in visual object recognition. J. Cogn. Neurosci. 15(4), 600–609 (2003)

    Article  MathSciNet  Google Scholar 

  33. Yao, J., Fidler, S., and Urtasun, R.: Describing the scene as a whole: joint object detection, scene classfication and semantic segmentation. In: CVPR (2012)

    Google Scholar 

  34. Kasther, S., Ungerleider, G.: Mechanisms of visual attention in the human cortex. Annu. Rev. Neural Sci. 23, 315–341 (2000)

    Google Scholar 

  35. Felzenszwalb, P., Huttenlocker, D.: Efficient graph-Based imagesegmentation. IJCV 2, 167–181 (2004)

    Article  Google Scholar 

  36. http://www.robots.ox.ac.uk/vgg/research/textclass/filters.html

  37. http://www.mit.edu/jmutch/fhlib

  38. Ojala, T., Pietikainen, M., Maenpaa, T.: Multi-resolution gray-scaleand rotation invariant texture classification with local binary patterns. PAMI 24(7), 971–986 (2002)

    Article  Google Scholar 

  39. Fenske, M.J., Aminoff, E., Gronau, N., Bar, M.: Top-down facilitation of visual object recognition: object-based and context-based contributions. Prog. Brain Res. 155, 3–21 (2006)

    Article  Google Scholar 

  40. Oliva, A., Torralba, A.: The role of context in object recognition. Trends Cogn. Sci. 11(2), 520–527 (2007)

    Article  Google Scholar 

  41. Desai, C., Ramanan, D., Fowlkes, C.C.: Discriminative models for multi-class object layout. IJCV 2, 169–176 (2012)

    Google Scholar 

  42. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned Salient Region Detection. In: CVPR (2009)

    Google Scholar 

  43. Rensink, R.A.: The dynamic representation of scenes. Visual Cognition 7(1/2/3), 17–42 (2000)

    Article  Google Scholar 

  44. Nistér, D., Stewénius, H.: Linear time maximally stable extremal regions. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 183–196. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  45. Matas, J., Chum, O., Urban, M., Pajdla, T: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. DSO National Laboratories, 20 Science Park Drive, Singapore, 118230, Singapore

    Xuhong Xiao, Gee Wah Ng, Yuan Sin Tan & Yeo Ye Chuan

Authors
  1. Xuhong Xiao
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  2. Gee Wah Ng
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  3. Yuan Sin Tan
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  4. Yeo Ye Chuan
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Corresponding author

Correspondence to Xuhong Xiao .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institue of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Xiao, X., Ng, G.W., Tan, Y.S., Ye Chuan, Y. (2015). Scene Parsing and Fusion-Based Continuous Traversable Region Formation. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_28

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16627-8

  • Online ISBN: 978-3-319-16628-5

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