Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream

Object detection is an essential component of many vision systems. For example, pedestrian detection is used in advanced driver assistance systems (ADAS) and advanced video surveillance systems (AVSS). Currently, most detectors (e.g. the YOLO - You Only Look Once - family) use deep convolutional neural networks. However, due to their high computational complexity, they are not able to process a very high-resolution video stream in real-time, especially within a limited energy budget. In this paper we present a hardware implementation of the well-known pedestrian detector with HOG (Histogram of Oriented Gradients) feature extraction and SVM (Support Vector Machine) classification. Our system running on
$\text{AMD} \text{Xilinx\ Zynq\ UltraScale}+ \text{MPSoC}$
(Multiprocessor System on Chip) device allows real-time processing of 4K resolution (UHD - Ultra High Definition,
$3840 \times 2160\ \text{pixels}$
) video for 60 frames per second. The system is capable of detecting a pedestrian in a single scale. The results obtained confirm the high suitability of reprogrammable devices in the real-time implementation of embedded vision systems.