Parallelization of AdaBoost algorithm on multi-core processors

Author

Chen, Yen-Kuang ; Li, Wenlong ; Tong, Xiaofeng

fYear

2008

fDate

8-10 Oct. 2008

Firstpage

275

Lastpage

280

Abstract

This paper examines and extracts the parallelism in the AdaBoost person detection algorithm on multi-core processors. As multi-core processors become pervasive, effectively executing many threads simultaneously is crucial in harnessing the computation power. Although the application exposes many levels of parallelism, none of them delivers a satisfactory scaling performance on newest multi-core processors due to load imbalance and parallel overhead. This paper demonstrates how to analyze the thread-level parallelism, and how to choose appropriate one to utilize current 4-core and 8-core processors. With careful optimization and parallelization, the AdaBoost person detection algorithm can efficiently utilize the power of multi-core processors, and now it is 7 times faster than the serial version.

Keywords

computer vision; learning (artificial intelligence); object detection; parallel processing; AdaBoost person detection algorithm; aggressive learning algorithm; computer vision; load imbalance; multicore processor; optimization; parallel overhead; Acceleration; Computer vision; Concurrent computing; Detection algorithms; Face detection; Multicore processing; Object detection; Parallel processing; Pervasive computing; Signal processing algorithms; computer vision; digital signal processors; parallel processing; pattern classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Systems, 2008. SiPS 2008. IEEE Workshop on

Conference_Location

Washington, DC

ISSN

1520-6130

Print_ISBN

978-1-4244-2923-3

Electronic_ISBN

1520-6130

Type

conf

DOI

10.1109/SIPS.2008.4671775

Filename

4671775