Fast Human Detection with Cascaded Ensembles on the GPU

Berkin Bilgic, Berthold K. P. Horn, Ichiro Masaki
Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA
IEEE Intelligent Vehicles Symposium (IV), 2010, Publisher: IEEE, Pages: 325-332


   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},





Source Source   



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.
No votes yet.
Please wait...

Recent source codes

* * *

* * *

HGPU group © 2010-2019 hgpu.org

All rights belong to the respective authors

Contact us: