论文标题
使用HOG和SVM算法的硬件系统实现用于人类检测
Hardware System Implementation for Human Detection using HOG and SVM Algorithm
论文作者
论文摘要
人类发现是一个流行的问题，已在许多应用中广泛使用。但是，包括计算中的复杂性，导致在实时应用中几乎没有实现人类检测系统。本文介绍了硬件的体系结构，这是一种在Modelim工具中模拟的人类检测系统。作为一名协同处理器，该系统的构建是为了从中央处理器单元（CPU）卸载并加快计算时间。静态输入图像的130x66 RGB像素分别使用定向梯度（HOG）算法的直方图和支持向量机（SVM）算法进行分类。结果，该系统的准确率达到84.35％。并且检测的时机在50MHz频率下降至0.757毫秒（使用MATLAB工具在软件中实现此系统时，该系统的时间更快）。
Human detection is a popular issue and has been widely used in many applications. However, including complexities in computation, leading to the human detection system implemented hardly in real-time applications. This paper presents the architecture of hardware, a human detection system that was simulated in the ModelSim tool. As a co-processor, this system was built to off-load to Central Processor Unit (CPU) and speed up the computation timing. The 130x66 RGB pixels of static input image attracted features and classify by using the Histogram of Oriented Gradient (HOG) algorithm and Support Vector Machine (SVM) algorithm, respectively. As a result, the accuracy rate of this system reaches 84.35 percent. And the timing for detection decreases to 0.757 ms at 50MHz frequency (54 times faster when this system was implemented in software by using the Matlab tool).
