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Appearance Based Vehicle Detection by Radar-Stereo Vision Integration

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 417))

Abstract

This paper proposes a novel method for appearance based vehicle detection by employing stereo vision system and radar units. In the vein of utilizing advanced driver assistance systems, detection and tracking of moving objects or particularly vehicles, represents an essential task. For the merits of such application, it has often been suggested to combine multiple sensors with complementary modalities. In accordance, in this work we utilize a stereo vision and two radar units, and fuse the corresponding modalities at the level of detection. Firstly, the algorithm executes the detection procedure based on stereo image solely, generating the information about vehicles’ position. Secondly, the final unique list of vehicles is obtained by overlapping the radar readings with the preliminary list obtained by stereo system. The stereo vision–based detection procedure consists of (i) edge processing plugging in also the information about disparity map, (ii) shape based vehicles’ contour extraction and (iii) preliminary vehicles’ positions generation. Since the radar readings are examined by overlapping them with the list obtained by stereo vision, the proposed algorithm can be considered as high level fusion approach. We analyze the performance of the proposed algorithm by performing the real-world experiment in highly dynamic urban environment, under significant illumination influences caused by sunny weather.

I. Petrović—This work has been supported by the European Regional Development Fund under the project Advanced technologies in power plants and rail vehicles.

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References

  1. Dickmanns, E.: The development of machine vision for road vehicles in the last decade. In: Intelligent Vehicle Symposium (IV), pp. 268–281. IEEE (2002)

    Google Scholar 

  2. Sivaraman, S., Trivedi, M.M.: Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Transactions on Intelligent Transportation Systems 14(4), 1773–1795 (2013)

    Article  Google Scholar 

  3. Wang, T., Zheng, N., Xin, J., Ma, Z.: Integrating millimeter wave radar with a monocular vision sensor for on-road obstacle detection applications. Sensors 11(9), 8992–9008 (2011)

    Article  Google Scholar 

  4. Wu, S., Decker, S., Chang, P., Camus, T., Eledath, J.: Collision sensing by stereo vision and radar sensor fusion. IEEE Transactions on Intelligent Transportation Systems 10(4), 606–614 (2009)

    Article  Google Scholar 

  5. Chavez-Garcia, R.O., Vu, T.-D., Burlet, J., Aycard, O.: Frontal object perception using radar and mono-vision. In: Intelligent Vehicles Symposium (IV), pp. 159–164. IEEE (2012)

    Google Scholar 

  6. Bertozzi, M., Bombini, L., Cerri, P., Medici, P., Antonello, P.C., Miglietta, M.: Obstacle detection and classification fusing radar and vision. In: Intelligent Vehicles Symposium (IV), pp. 608–613. IEEE (2008)

    Google Scholar 

  7. Liu, F., Sparbert, J., Stiller, C.: IMMPDA vehicle tracking system using asynchronous sensor fusion of radar and vision. In: Intelligent Vehicles Symposium (IV), pp. 168–173. IEEE (2008)

    Google Scholar 

  8. Richter, E., Schubert, R., Wanielik, G.: Radar and vision-based data fusion - advanced filtering techniques for a multi-object vehicle tracking system. In: Intelligent Vehicles Symposium (IV), pp. 120–125. IEEE (2008)

    Google Scholar 

  9. Herbst, E., Ren, X., Fox, D.: RGB-D flow: dense 3-D motion estimation using color and depth. In: Int. Conf. on Rob. and Autom. (ICRA), pp. 2276–2282 (2013)

    Google Scholar 

  10. Ohn-bar, E., Sivaraman, S., Trivedi, M.: Partially occluded vehicle recognition and tracking in 3D. In: Intell. Veh. Symposium (IV), pp. 1350–1355 (2013)

    Google Scholar 

  11. Lefaudeux, B., Nashashibi, F.: Real-time visual perception: detection and localisation of static and moving objects from a moving stereo rig. In: Intelligent Transportation Systems Conference (ITSC), pp. 522–527 (2012)

    Google Scholar 

  12. Lenz, P., Ziegler, J., Geiger, A., Roser, M.: Sparse scene flow segmentation for moving object detection in urban environments. In: Intelligent Vehicles Symposium (IV), pp. 926–932 (2011)

    Google Scholar 

  13. Wedel, A., Brox, T., Vaudrey, T., Rabe, C., Franke, U., Cremers, D.: Stereoscopic scene flow computation for 3D motion understanding. International Journal of Computer Vision 95(1), 29–51 (2011)

    Article  MATH  Google Scholar 

  14. Cao, X., Wu, C., Yan, P., Li, X.: Linear svm classification using boosting hog features for vehicle detection in low-altitude airborne videos. In: IEEE International Conference on Image Processing (ICIP), pp. 2421–2424 (2011)

    Google Scholar 

  15. Marin, J., Vazquez, D., Lopez, A., Amores, J., Leibe, B.: Random forests of local experts for pedestrian detection. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 2592–2599 (2013)

    Google Scholar 

  16. Broggi, A., Cappalunga, A., Caraffi, C., Cattani, S., Ghidoni, S., Grisleri, P., Porta, P.P., Posterli, M., Zani, P.: TerraMax vision at the Urban challenge 2007. IEEE Trans. on Intell. Transp. Systems 11(1), 194–205 (2010)

    Article  Google Scholar 

  17. Barrois, B., Hristova, S., Wohler, C., Kummert, F., Hermes, C.: 3D pose estimation of vehicles using a stereo camera. In: Intelligent Vehicles Symposium (IV), pp. 267–272 (2009)

    Google Scholar 

  18. Fang, Y., Masaki, I., Horn, B.: Depth-Based Target Segmentation for Intelligent Vehicles: Fusion of Radar and Binocular Stereo. IEEE Transactions on Intelligent Transportation Systems 3(3), 196–202 (2002)

    Article  Google Scholar 

  19. Kato, T., Ninomiya, Y., Masaki, I.: An Obstacle Detection Method by Fusion of Radar and Motion Stereo. IEEE Transactions on Intelligent Transportation Systems 3(3), 182–187 (2002)

    Article  Google Scholar 

  20. Alessandretti, G., Broggi, A., Cerri, P.: Vehicle and guard rail detection using radar and vision data fusion. IEEE Trans. on Intelligent Transp. Systems 8(1), 95–105 (2007)

    Article  Google Scholar 

  21. Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI–8(6), 679–698 (1986)

    Article  Google Scholar 

  22. Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: Ros: an open-source robot operating system (2009)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, Departement of Control and Computer Engineering, University of Zagreb, Unska 3, Zagreb, Croatia

    Marko Obrvan, Josip Ćesić & Ivan Petrović

Authors
  1. Marko Obrvan
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  2. Josip Ćesić
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  3. Ivan Petrović
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Corresponding author

Correspondence to Josip Ćesić .

Editor information

Editors and Affiliations

  1. Info Syst Dept;Campus de Azurém, University of Minho, Guimarães, Portugal

    Luís Paulo Reis

  2. Electrical and Computer Engineering Dept, University of Porto, Porto, Portugal

    António Paulo Moreira

  3. Instituto Superior Tecnico - Torre Norte, University of Lisboa, Lisboa, Portugal

    Pedro U. Lima

  4. Engineering Dept., E. Ada Byron, Univ of Zaragoza, Computer & Systems, Zaragoza, Spain

    Luis Montano

  5. Automatic Control and Systems Eng. Dept., University of Malaga, Superior Technical, Málaga, Spain

    Victor Muñoz-Martinez

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Obrvan, M., Ćesić, J., Petrović, I. (2016). Appearance Based Vehicle Detection by Radar-Stereo Vision Integration. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-319-27146-0_34

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  • DOI: https://doi.org/10.1007/978-3-319-27146-0_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27145-3

  • Online ISBN: 978-3-319-27146-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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