Summary
This paper presents a framework to detect moving objects based on the recognition of moving features in the images. The classification scheme is based on a complete probabilistic representation of feature locations that relates the vehicle motion with the visual information. Experimental evaluation under different settings in an outdoor, urban environment shows the performance of the proposed architecture.
This work is supported by the ARC Centre of Excellence programme, funded by the Australian Research Council (ARC) and the New South Wales state government.
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References
ACFR, The University of Sydney and LCR, Universidad Nacional del Sur. PAATV/UTE Projects (2006)
Blackman, S., Popolif, R.: Design and Analysis of Modern Tracking Systems. Artech House Radar Library, New York (1999)
Bouguet, J.-Y.: Camera Calibration Toolbox for Matlab (2007)
Giachetti, A., Campani, M., Torre, V.: The Use of Optical Flow for Road Navigation. IEEE Transactions on Robotics and Automation 14(1), 34–48 (1998)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)
Krishna, K.M., Kalra, P.K.: Detection, Tracking and Avoidance of Multiple Dynamic Objects. Journal of Intelligent and Robotic Systems 33, 371–408 (2002)
Kruger, W., Enkelmann, W., Rossle, S.: Real-time Estimation and Tracking of Optical Flow Vectors for Obstacle Detection. In: Intelligent Vehicles 1995 Symposium (1995)
Ma, Y., Soatto, S., Kosecka, J., Sastry, S.: An Invitation to 3-D Vision: From Images to Geometric Models. Springer, New York (2004)
Monteiro, G., Premebida, C., Peixoto, P., Nunes, U.: Tracking and Classification of Dynamic Obstacles Using Laser Range Finder and Vision. In: IEEE/RSJ International Workshop on Intelligent Robots and Systems, Beijing, China, IEEE, Los Alamitos (2006)
Nieto, J.: Detailed Environment Representation for the SLAM Problem. PhD thesis, University of Sydney, Australia (2005)
Sun, Z., Bebis, G., Miller, R.: On-Road Vehicle Detection: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5), 694–711 (2006)
Wang, B.: Simultaneous Localization, Mapping and Moving Object Tracking. PhD thesis, Robotics Institute, Carnegie Mellon University, USA (2004)
Woelk, F., Koch, R.: Fast Monocular Bayesian Detection of Independently Moving Objects by a Moving Observer. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 27–35. Springer, Heidelberg (2004)
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Katz, R., Frank, O., Nieto, J., Nebot, E. (2008). Dynamic Obstacle Detection Based on Probabilistic Moving Feature Recognition. In: Laugier, C., Siegwart, R. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75404-6_8
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DOI: https://doi.org/10.1007/978-3-540-75404-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75403-9
Online ISBN: 978-3-540-75404-6
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