TechRxiv
_CPS_IoT_2022_Real_time_HOG_SVM_based_object_detection_using_SoC_FPGA_for_a_UHD_video_stream.pdf (426.59 kB)
Download file

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

Download (426.59 kB)
preprint
posted on 25.04.2022, 20:32 by Mateusz Wasala, Tomasz KryjakTomasz Kryjak
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.

Funding

The work presented in this paper was supported by the National Science Centre project no. 2016/23/D/ST6/01389 entitled ''The development of computing resources organization in latest generation of heterogeneous reconfigurable devices enabling real-time processing of UHD/4K video stream''

History

Email Address of Submitting Author

tomasz.kryjak@agh.edu.pl

ORCID of Submitting Author

0000-0001-6798-4444

Submitting Author's Institution

AGH University of Science and Technology

Submitting Author's Country

Poland