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|a Bilgic, Berkin
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Horn, Berthold K. P.
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|a Bilgic, Berkin
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|a Horn, Berthold Klaus Paul
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|a Masaki, Ichiro
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|a Horn, Berthold Klaus Paul
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|a Masaki, Ichiro
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|a Fast Human Detection With Cascaded Ensembles On The GPU
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|b Institute of Electrical and Electronics Engineers (IEEE),
|c 2012-07-30T13:15:21Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/71884
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|a 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.
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|a en_US
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|a Article
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|t 2010 IEEE Intelligent Vehicles Symposium (IV)
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