Abstract
This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach.
This work has been partially supported by the Spanish Government under project TRA2007-62526/AUT; research programme Consolider-Ingenio 2010: MIPRCV (CSD2007-00018); and Catalan Government under project CTP 2008ITT 00001.
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References
Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A., Jarawan, E., Mathers, C.: World Report on road traffic injury prevention. World Health Organization, Geneva (2004)
Coulombeau, P., Laurgeau, C.: Vehicle yaw, pitch, roll and 3D lane shape recovery by vision. In: Proc. IEEE Intelligent Vehicles Symposium, Versailles, France, pp. 619–625 (2002)
Liang, Y., Tyan, H., Liao, H., Chen, S.: Stabilizing image sequences taken by the camcorder mounted on a moving vehicle. In: Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Shangai, China, pp. 90–95 (2003)
Bertozzi, M., Broggi, A., Carletti, M., Fascioli, A., Graf, T., Grisleri, P., Meinecke, M.: IR pedestrian detection for advaned driver assistance systems. In: Proc. 25th. Pattern Recognition Symposium, Magdeburg, Germany, pp. 582–590 (2003)
Nedevschi, S., Vancea, C., Marita, T., Graf, T.: Online extrinsic parameters calibration for stereovision systems used in far-range detection vehicle applications. IEEE Trans. on Intelligent Transportation Systems 8(4), 651–660 (2007)
Labayrade, R., Aubert, D., Tarel, J.: Real time obstacle detection in stereovision on non flat road geometry through ‘V-disparity’ representation. In: Proc. IEEE Intelligent Vehicles Symposium, Versailles, France, pp. 646–651 (2002)
Bertozzi, M., Binelli, E., Broggi, A., Del Rose, M.: Stereo vision-based approaches for pedestrian detection. In: Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, San Diego, USA (2005)
Labayrade, R., Aubert, D.: A single framework for vehicle roll, pitch, yaw estimation and obstacles detection by stereovision. In: Proc. IEEE Intelligent Vehicles Symposium, Columbus, OH, USA, pp. 31–36 (2003)
Sappa, A., Dornaika, F., Ponsa, D., Gerónimo, D., López, A.: An efficient approach to on-board stereo vision system pose estimation. IEEE Trans. on Intelligent Transportation Systems 9(3), 476–490 (2008)
Dornaika, F., Sappa, A.: A featureless and stochastic approach to on-board stereo vision system pose. Image and Vision Computing 27(9), 1382–1393 (2009)
Suzuki, T., Kanade, T.: Measurement of vehicle motion and orientation using optical flow. In: Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Tokyo, Japan, pp. 25–30 (1999)
Stein, G., Mano, O., Shashua, A.: A robust method for computing vehicle ego-motion. In: IEEE Intelligent Vehicles Symposium, Dearborn Michigan, USA, pp. 362–368 (2000)
Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L 1 optical flow. In: Proc. 29th Annual Symposium of the German Association for Pattern Recognition, Heidelberg, Germany, pp. 214–223 (2007)
Wedel, A., Pock, T., Zach, C., Cremers, D., Bischof, H.: An improved algorithm for TV-L1 optical flow. In: Proc. of the Dagstuhl Motion Workshop, Dagstuhl Castle, Germany, pp. 23–45 (2008)
Horn, B.K.P., Schunk, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)
Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imaging Vis. 20(1-2), 89–97 (2004)
Zhaoxue, C., Pengfei, S.: Efficient method for camera calibration in traffic scenes. Electronics Letters 40(6), 368–369 (2004)
Rasmussen, C.: Grouping dominant orientations for ill-structured road following. In: Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, Washington, USA, pp. 470–477 (2004)
Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Graphics and Image Processing 24(6), 381–395 (1981)
Vaudrey, T., Rabe, C., Klette, R., Milburn, J.: Differences between stereo and motion behaviour on synthetic and real-world stereo sequences. In: Proc. Image and Vision Computing New Zealand, Christchurch, New Zealand, pp. 1–6 (2008)
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Onkarappa, N., Sappa, A.D. (2010). On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13772-3_24
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DOI: https://doi.org/10.1007/978-3-642-13772-3_24
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