By this paper, the major goal is to investigate the Multi-CPU/FPGA SoC (System on Chip)
design flow and to transfer a know-how and skills to rapidly design embedded real-time
vision system. Our aim is to show how the use of these devices can be benefit for system
level integration since they make possible simultaneous hardware and software
development. We take the facial detection and pretreatments as case study since they
have a great potential to be used in several applications such as video surveillance,
building access control and criminal identification. The designed system use the Xilinx
Zedboard platform. The last is the central element of the developed vision system. The
video acquisition is performed using either standard webcam connected to the Zedboard
via USB interface or several camera IP devices. The visualization of video content and
intermediate results are possible with HDMI interface connected to HD display. The
treatments embedded in the system are as follow: (i) pre-processing such as edge
detection implemented in the ARM and in the reconfigurable logic, (ii) software
implementation of motion detection and face detection using either ViolaJones or LBP
(Local Binary Pattern), and (iii) application layer to select processing application and to
display results in a web page. One uniquely interesting feature of the proposed system is
that two functions have been developed to transmit data from and to the VDMA port.
With the proposed optimization, the hardware implementation of the Sobel filter takes 27
ms and 76 ms for 640x480, and 720p resolutions, respectively. Hence, with the FPGA
implementation, an acceleration of 5 times is obtained which allow the processing of 37
fps and 13 fps for 640x480, and 720p resolutions, respectively.
Maher Jridi and Ayman Alfalou, "Rapid prototyping of SoC-based real-time vision system: application to image preprocessing and face detection," Proc. SPIE 10203, Pattern Recognition and Tracking XXVIII, 102030M (Presented at SPIE Defense + Security: April 13, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2264712.
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