Accelerated Training of Linear Object Detectors

Charles Dubout, Francois Fleuret; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2013, pp. 572-577

Abstract


We describe a general and exact method to speed up the training of linear object detection systems operating in a sliding, multi-scale window fashion, such as deformable part-based models. Our approach consists of reformulating the computation of the gradient as a convolution, and making use of properties of the Fourier transform to obtain a speedup factor proportional to the linear filters' sizes. This technique does not rely on the sparsity induced by a specific loss, nor on a stochastic sub-sampling of the training examples. Experiments on the PASCAL VOC benchmark show a speedup factor of more than one order of magnitude compared to a standard exact generic method.

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[bibtex]
@InProceedings{Dubout_2013_CVPR_Workshops,
author = {Dubout, Charles and Fleuret, Francois},
title = {Accelerated Training of Linear Object Detectors},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2013}
}