Fast Human Detection with Cascaded Ensembles on the GPU
Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA
IEEE Intelligent Vehicles Symposium (IV), 2010, Publisher: IEEE, Pages: 325-332
@conference{bilgic2010fast,
title={Fast human detection with cascaded ensembles on the GPU},
author={Bilgic, B. and Horn, B.K.P. and Masaki, I.},
booktitle={Intelligent Vehicles Symposium (IV), 2010 IEEE},
pages={325–332},
issn={1931-0587},
organization={IEEE}
}
We investigate a fast pedestrian localization framework that integrates the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features on a data parallel architecture. The salient features of humans are captured by HoG blocks of variable sizes and locations which are chosen by the AdaBoost algorithm from a large set of possible blocks. We use the integral image representation for histogram computation and a rejection cascade in a sliding-windows manner, both of which can be implemented in a data parallel fashion. Utilizing the NVIDIA CUDA framework to realize this method on a Graphics Processing Unit (GPU), we report a speed up by a factor of 13 over our CPU implementation. For a 1280×960 image our parallel technique attains a processing speed of 2.5 to 8 frames per second depending on the image scanning density, which is similar to the recent GPU implementation of the original HoG algorithm in.
March 14, 2011 by hgpu
