Abstract
This paper presents a practical system for vision-based traffic scene analysis from a moving vehicle based on a cognitive feedback loop which integrates real-time geometry estimation with appearance-based object detection. We demonstrate how those two components can benefit from each other’s continuous input and how the transferred knowledge can be used to improve scene analysis. Thus, scene interpretation is not left as a matter of logical reasoning, but is instead addressed by the repeated interaction and consistency checks between different levels and modes of visual processing. As our results show, the proposed tight integration significantly increases recognition performance, as well as overall system robustness. In addition, it enables the construction of novel capabilities such as the accurate 3D estimation of object locations and orientations and their temporal integration in a world coordinate frame. The system is evaluated on a challenging real-world car detection task in an urban scenario.
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References
Andreone, L., Antonello, P.C., Bertozzi, M., Broggi, A., Fascioli, A., Ranzato, D.: Vehicle detection and localization in infra-red images. In: Intel. Vehicles Symp. 2002 (2002)
Betke, M., Haritaoglu, E., Davis, L.S.: Real-time multiple vehicle tracking from a moving vehicle. MVA 12(2), 69–83 (2000)
Cornelis, N., Cornelis, K., Van Gool, L.: Fast compact city modeling for navigation pre-visualization. In: CVPR 2006 (2006)
Fischler, M., Bolles, R.: Random sampling consensus: A paradigm for model fitting with application to image analysis and automated cartography. Comm. ACM 24, 381–395 (1981)
Gavrila, D., Philomin, V.: Real-time object detection for smart vehicles. In: ICCV 1999, pp. 87–93 (1999)
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2000)
Koller, D., Daniilidis, K., Nagel, H.-H.: Model-based object tracking in monocular image sequences of road traffic scenes. IJCV 10(3), 257–281 (1993)
Leibe, B., Schiele, B.: Scale invariant object categorization using a scale-adaptive mean-shift search. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 145–153. Springer, Heidelberg (2004)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: CVPR 2005 (2005)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2) (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI 27(10) (2005)
Mittal, A., Davis, L.S.: M2 tracker: A multi-view approach to segmenting and tracking people in a cluttered scene. IJCV 51(3), 183–203 (2003)
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. Technical Report AIM-2005-025, MIT AI Lab (2005)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47, 7–42 (2002)
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© 2006 Springer-Verlag Berlin Heidelberg
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Leibe, B., Cornelis, N., Cornelis, K., Van Gool, L. (2006). Integrating Recognition and Reconstruction for Cognitive Traffic Scene Analysis from a Moving Vehicle. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_20
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DOI: https://doi.org/10.1007/11861898_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44412-1
Online ISBN: 978-3-540-44414-5
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