  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 use deep convolutional neural networks (e.g., the YOLO -- You Only
Look Once -- family), which, however, due to their high computational
complexity, 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 AMD Xilinx Zynq UltraScale+
MPSoC (Multiprocessor System on Chip) device allows real-time processing of 4K
resolution (UHD -- Ultra High Definition, 3840 x 2160 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.
