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Published in:
2014 | OriginalPaper | Chapter
Active Deformable Part Models Inference
Authors : Menglong Zhu, Nikolay Atanasov, George J. Pappas, Kostas Daniilidis
Published in: Computer Vision – ECCV 2014
Publisher: Springer International Publishing
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This paper presents an active approach for part-based object detection, which optimizes the order of part filter evaluations and the time at which to stop and make a prediction. Statistics, describing the part responses, are learned from training data and are used to formalize the part scheduling problem as an
offline
optimization. Dynamic programming is applied to obtain a policy, which balances the number of part evaluations with the classification accuracy. During inference, the policy is used as a look-up table to choose the
part order
and the
stopping time
based on the observed filter responses. The method is faster than cascade detection with deformable part models (which does not optimize the part order) with negligible loss in accuracy when evaluated on the PASCAL VOC 2007 and 2010 datasets.