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CVPR
2010
IEEE

Cascade Object Detection with Deformable Part Models

14 years 7 months ago
Cascade Object Detection with Deformable Part Models
We describe a general method for building cascade classifiers from part-based deformable models such as pictorial structures. We focus primarily on the case of star-structured models and show how a simple algorithm based on partial hypothesis pruning can speed up object detection by more than one order of magnitude, without sacrificing the accuracy of the results. In our algorithm partial hypothesis are pruned with a sequence of thresholds. In analogy to PAC learning we introduce the notion of probably approximately admissible (PAA) thresholds. Such thresholds provide theoretical guarantees on the performance of the cascade method and can be computed from a small sample of positive examples. Finally, we outline a cascade detection algorithm for a general class of grammar based models. This includes not only tree-structured pictorial structures, but also richer models that can represent each part recursively as a mixture of other parts.
Pedro Felzenszwalb, Ross Girshick, David McAlleste
Added 29 Mar 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Pedro Felzenszwalb, Ross Girshick, David McAllester
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