Title :
Fast human detection with cascaded ensembles on the GPU
Author :
Bilgic, Berkin ; Horn, Berthold K P ; Masaki, Ichiro
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA
Abstract :
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.
Keywords :
computer graphic equipment; coprocessors; feature extraction; image representation; object detection; parallel architectures; AdaBoost algorithm; GPU; NVIDIA CUDA framework; data parallel architecture; graphics processing unit; histograms of oriented gradient; human detection; image representation; image scanning density; pedestrian localization; sliding windows; Central Processing Unit; Concurrent computing; Detectors; Graphics; Histograms; Humans; Intelligent vehicles; Support vector machine classification; Support vector machines; USA Councils;
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2010 IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-7866-8
DOI :
10.1109/IVS.2010.5548145
