Towards Multi-View Object Class Detection

Alexander Thomas, Vittorio Ferrari, Bastian Leibe, Tinne Tuytelaars, Bernt Schiele, Luc Van Gool
Conference on Computer Vision and Pattern Recognition (CVPR'06)

We present a novel system for generic object class de- tection. In contrast to most existing systems which focus on a single viewpoint or aspect, our approach can detect ob- ject instances from arbitrary viewpoints. This is achieved by combining the Implicit Shape Model for object class de- tection proposed by Leibe and Schiele with the multi-view specific object recognition system of Ferrari et al. After learning single-view codebooks, these are inter- connected by so-called activation links, obtained through multi-view region tracks across different training views of individual object instances. During recognition, these inte- grated 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.

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