Real-time moving object detection using FPGA


Real-time moving object detection application is developed using Computer Vision using Python in FPGA


python language with fpga [PYNQ-Z2]

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Package Includes:

  • Complete Hardware Kit

  • Demo Video

  • Abstract

  • Reference Paper

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Artificial intelligence is the current trend in this technological world, Every industry is trying to build their own development boards for AI. In that list, Xilinx introducing FPGA board PYNQ with ZYNQ FPGA, the board is compatible with python programming. Python can be programmed into the PYNQ board using Jupyter Notebook.


In this project, PYNQ Z2 board is used, the python program is developed using Jupyter Notebook by connecting PYNQ with the network through Ethernet to get the IP Address of the board. So that Jupyter Notebook can be opened using that IP Address. Real-time moving object detection application is developed using Computer Vision using Python in FPGA.

Existing System

In the existing system, deep learning applications are developed on other boards such as Raspberry Pi. FPGA doesn’t have python support.

Proposed System      

In this proposed system, Python is developed in FPGA to use the FPGA core computational power. PYNQ Z2 has SD Card Interface, where SDCard is booted with an image provided by Xilinx for PYNQ Z2, makes to work with Jupyter Notebook.

Connection description

SD Card is booted with PYNQ-Z2 image, inserted into the SD Card slot of the PYNQ board. PYNQ Z2 is connected with the router through Ethernet cable. USB camera is connected with the PYNQ USB Port. HDMI Monitor is connected with the HDMI OUT port of the PYNQ board.

Project description

Moving object detection application is completely developed using computer Vision with Python Programming through Jupyter Notebook. After program execution, USB Camera is fed through the USB port and gets displayed in the HDMI Monitor connected in HDMI Output port. On the obtaining video frames, boundary box is plotted for every moving object in the video frame.

Hardware required

Software required

  • PYNQ Z2 boot image
  • SD Card Formatter
  • Etcher/Win32 disk imager


By using FPGA for Deep learning projects, Computational process of the hardware is increased with a high frame rate. Python compatibility makes this FPGA PYNQ board perform AI applications

Additional information

Weight 0.000000 kg


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