Weakly Supervised Shape Based Object Detection with Particle Filter

11 years 3 months ago
Weakly Supervised Shape Based Object Detection with Particle Filter
Abstract. We describe an efficient approach to construct shape models composed of contour parts with partially-supervised learning. The proposed approach can easily transfer parts structure to different object classes as long as they have similar shape. The spatial layout between parts is described by a non-parametric density, which is more flexible and easier to learn than commonly used Gaussian or other parametric distributions. We express object detection as state estimation inference executed using a novel Particle Filters (PF) framework with static observations, which is quite different from previous PF methods. Although the underlying graph structure of our model is given by a fully connected graph, the proposed PF algorithm efficiently linearizes it by exploring the conditional dependencies of the nodes representing contour parts. Experimental results demonstrate that the proposed approach can not only yield very good detection results but also accurately locates contours of ...
Added 11 Jul 2010
Updated 11 Jul 2010
Type Conference
Year 2010
Where ECCV
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