Hardware System Implementation for Human Detection using HOG and SVM Algorithm
This work addresses the computational bottleneck for real-time human detection in applications like surveillance, but it is incremental as it applies existing methods to a hardware implementation.
The paper tackled the problem of real-time human detection by implementing a hardware system using HOG and SVM algorithms, achieving 84.35% accuracy and reducing detection time to 0.757 ms, which is 54 times faster than a software implementation.
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).