Towards multi-view object class detection

Alexander Thomas 1 Vittorio Ferrari 2 Bastian Leibe 3 Tinne Tuytelaars 4 Bernt Schiele 5 Luc Van Gool 3
1 KU Leuven - Katholieke Universiteit Leuven
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
3 ETH Zürich - Eidgenössische Technische Hochschule [Zürich]
4 ESAT - Department of Electrical Engineering [Leuven]
5 Department of Computer Science
Abstract : We present a novel system for generic object class detection. In contrast to most existing systems which focus on a single viewpoint or aspect, our approach can detect object instances from arbitrary viewpoints. This is achieved by combining the Implicit Shape Model for object class detection proposed by Leibe and Schiele with the multi-view specific object recognition system of Ferrari et al. After learning single-view codebooks, these are interconnected by so-called activation links, obtained through multi-view region tracks across different training views of individual object instances. During recognition, these integrated codebooks work together to determine the location and pose of the object. Experimental results demonstrate the viability of the approach and compare it to a bank of independent single-view detectors.
keyword : object detection multiple views local features
Type de document :
Communication dans un congrès
IEEE Conference on Computer Vision & Pattern Recognition (CPRV '06), Jun 2006, New York, United States. IEEE Computer Society, 2, pp.1589, 2006, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1640946〉. 〈10.1109/CVPR.2006.311〉
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Alexander Thomas, Vittorio Ferrari, Bastian Leibe, Tinne Tuytelaars, Bernt Schiele, et al.. Towards multi-view object class detection. IEEE Conference on Computer Vision & Pattern Recognition (CPRV '06), Jun 2006, New York, United States. IEEE Computer Society, 2, pp.1589, 2006, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1640946〉. 〈10.1109/CVPR.2006.311〉. 〈inria-00548577〉

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