  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).
