关键词: GPU, Adaboost, CUDA, 人脸检测

Abstract:

The Adaboost algorithm is an efficient method for target detection. Since 2001, a lot of improvement has been proposed in order to improve the detection accuracy and the scope of application. However, the training of an Adaboost classifier is still a timeconsuming process. Currently, research on the combination of CUDA and Adaboost focuses on how to accelerate the process of object detection with existing classifiers. This paper analyzes the Adaboost algorithm,and the experimental results indicate that calculating the values of simple features and training weak classifiers consume most of the time. Then on the basis of the GPU hardware structure and CUDA, this paper presents a parallel implementation of the Adaboost algorithm aiming at accelerating the whole training process, and optimizes the data storage structure and data access efficiency. We test the implementation on a sample set which contains 38,400 samples of size 19×19 .The results show that 8.1 times speedup has been achieved with a good detection performance.

Key words: GPU;Adaboost;CUDA;face detection