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).
翻译:人类探测是一个广受欢迎的问题,在许多应用中广泛使用。然而,包括复杂的计算方法,导致人类探测系统几乎无法实时应用。本文介绍了硬件结构,这是在ModelSim工具中模拟的人类探测系统。作为一个共同处理器,这个系统是用来卸载到中央处理器股(CPU)并加快计算时间的。130x66 RGB 静态输入图像像素吸引了各种特征,并分别使用东方梯度(HOG)算法和支助矢量机(SVM)算法(SVM)算法进行分类。结果,这个系统的精确率达到84.35%。探测时间在50MHz频率下降至0.757米(当该系统在软件中使用Matlab工具实施时速度加快了54倍)。