Accelerating AdaBoost Algorithm Using GPU for Multi-Object Recognition

Traditionally, an adaptive boosting (AdaBoost) algorithm is used for object recognition because of its prevalent usage and well-trained results. However, because the computation of AdaBoost is extremely time-consuming, it is difficult to guarantee that the computations reflect the latest information in real time. To speed-up the operation, the original AdaBoost algorithm was accelerated with a graphics processing unit (GPU). In this study, Compute Unified Device Architecture (CUDA) was used to accelerate two parts of the AdaBoost algorithm, including feature extraction and training, by applying various strategies to system components such as how the data is put in the memory, amount of CUDA streams, trunk size, and block size. In Feature Extraction of the car datasets, the most time-consuming step feature-value computation is 47.18 times faster than the CPU version. For AdaBoost Training, the total execution is accelerated by 34.23 times.