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Hierarchical Cascade of Classifiers for Efficient Poselet Evaluation

Chen, Bo and Perona, Pietro and Bourdev, Lubomir (2014) Hierarchical Cascade of Classifiers for Efficient Poselet Evaluation. In: Proceedings of the British Machine Vision Conference 2014. BMVA Press , Durham, UK, Art. No. 96. ISBN 1901725529. http://resolver.caltech.edu/CaltechAUTHORS:20190327-125836326

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Abstract

Poselets have been used in a variety of computer vision tasks, such as detection, segmentation, action classification, pose estimation and action recognition, often achieving state-of-the-art performance. Poselet evaluation, however, is computationally intensive as it involves running thousands of scanning window classifiers. We present an algorithm for training a hierarchical cascade of part-based detectors and apply it to speed up poselet evaluation. Our cascade hierarchy leverages common components shared across poselets. We generate a family of cascade hierarchies, including trees that grow logarithmically on the number of poselet classifiers. Our algorithm, under some reasonable assumptions, finds the optimal tree structure that maximizes speed for a given target detection rate. We test our system on the PASCAL dataset and show an order of magnitude speedup at less than 1% loss in AP.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.5244/c.28.31DOIArticle
ORCID:
AuthorORCID
Chen, Bo0000-0001-5566-7361
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
Record Number:CaltechAUTHORS:20190327-125836326
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190327-125836326
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94218
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:27 Mar 2019 20:35
Last Modified:27 Mar 2019 20:35

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