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2010 | OriginalPaper | Chapter

Part-Based Feature Synthesis for Human Detection

Authors: Aharon Bar-Hillel, Dan Levi, Eyal Krupka, Chen Goldberg

Published in: Computer Vision – ECCV 2010

Publisher: Springer Berlin Heidelberg

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We introduce a new approach for learning part-based object detection through feature synthesis. Our method consists of an iterative process of feature generation and pruning. A feature generation procedure is presented in which basic part-based features are developed into a feature hierarchy using operators for part localization, part refining and part combination. Feature pruning is done using a new feature selection algorithm for linear SVM, termed Predictive Feature Selection (PFS), which is governed by weight prediction. The algorithm makes it possible to choose from

O

(10

6

) features in an efficient but accurate manner. We analyze the validity and behavior of PFS and empirically demonstrate its speed and accuracy advantages over relevant competitors. We present an empirical evaluation of our method on three human detection datasets including the current de-facto benchmarks (the INRIA and Caltech pedestrian datasets) and a new challenging dataset of children images in difficult poses. The evaluation suggests that our approach is on a par with the best current methods and advances the state-of-the-art on the Caltech pedestrian training dataset.

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Metadata
Title
Part-Based Feature Synthesis for Human Detection
Authors
Aharon Bar-Hillel
Dan Levi
Eyal Krupka
Chen Goldberg
Copyright Year
2010
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-15561-1_10

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