以GPU加速Adaboost為基底之多物件辨識系統

蔡品儀

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以GPU加速Adaboost為基底之多物件辨識系統

A GPU-Accelerated Object Recognition System Using Adaboost Algorithm

蔡品儀 , Masters　　Advisor：許雅三　　

英文

物件辨識；圖形處理器；機器學習； object recognition ； GPU ； adaboost

Abstract │ Reference (67) │ Altmetrics

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Recently, programming on GPU has become the general solution of high performance computing. Various applications and frameworks are developed to utilize the power of GPU. CUDA, proposed by Nvidia, enables programmers who are not major in computer vision also benefit from GPU easily. CUDA is only available on Nvidia’s GPU; for other GPUs, OpenCL can be applied to deal with the similar work. OpenCL supports cross-platform programming and also can cooperate with CPU.
    However, the powerful advantage is obvious only when the data is massive. If the data is not enough to cover the communication and overhead of kernel launching, the performance of GPU may be worse than the version with single CPU thread due to GPU’s lower clock-rate and necessary data transportation. 
    ADAS (Advanced Driver Assistance Systems) is used to remind or help drivers to act while the emergency occurs in the surroundings. For the safety, the ADAS is equipped with a front-view camera to detect other cars, pedestrian and traffic signs around. Traditionally, Adaboost algorithm is often applied to object recognition because of its wider 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 make sure the safety when the car is moving and the environment keeps changing, we would like to accelerate the original object recognition system with GPU. In our system, we applied CUDA to accelerate Feature Extraction and Adaboost Training. We do not focus on Adaboost Testing since it is as complex as Adaboost Training is. For these two parts, we adopted different strategies such as how the data is put in the memory, the amount of CUDA streams, trunk size, the size of block, etc. 
Finally, our system can gain 6.12x in Feature Extraction and 34.53x in Adaboost Training on Nvidia K20c. The accuracy and safety of ADAS become higher because the surroundings can be sensed more distinctly with Adaboost algorithm in GPU computing.

Reference ( 67 ) 〈TOP〉

[1] Freescale,”Advanced Driver Assistance Systems (ADAS),”
連結：
[3] Heidari. H. Chalechale. A. and Mohammadabadi. A.A. ," Accelerating of color moments and texture features extraction using GPU based parallel computing ," 8th Iranian Conference on Machine Vision and Image Processing (MVIP) , pp. 430 - 435 , Sept. 2013
連結：
[5] Malakar, R. and Vydyanathan. N. ," A CUDA-enabled Hadoop Cluster for Fast Distributed Image Processing ," National Conference on Parallel Computing Technologies (PARCOMPTECH), pp. 1-5 , Feb. 2013
連結：
[8] Sharma, B. , Thota. R. , Vydyanathan. N. and Kale. Amit ," Towards a Robust, Real-time Face Processing System using CUDA-enabled GPUs , " International Conference on High Performance Computing (HiPC) , pp. 368-377 , Dec. 2009
連結：
[12] Ming-Ke Zhou, Fei Yin and Cheng-Lin Liu ," GPU-Based Fast Training of Discriminative Learning Quadratic Discriminant Function for Handwritten Chinese Character Recognition ," 12th International Conference on Document Analysis and Recognition (ICDAR) , pp. 842-846 , Aug. 2013
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