A Real-Time Implementation of Moving Object Action Recognition System Based on Motion Analysis

This paper proposes a PixelStreams-based FPGA implementation of a real-time system that can detect and recognize human activity using Handel-C. In the first part of our work, we propose a GUI programmed using Visual C++ to facilitate the implementation for novice users. Using this GUI, the user can program/erase the FPGA or change the parameters of different algorithms and filters. The second part of this work details the hardware implementation of a real-time video surveillance system on an FPGA, including all the stages, i.e., capture, processing, and display, using DK IDE. The targeted circuit is an XC2V1000 FPGA embedded on Agility’s RC200E board. The PixelStreams-based implementation was successfully realized and validated for real-time motion detection and recognition

A high-level environment for FPGA neural network implementation

This work aims at the realization of a high-level environment to facilitate and accelerate the neural network implementation on FPGAs. A parameterizable tool was designed to generate a neural multi-layer network implementation through the use of Handel-C language. The algorithm used for the training is the back-propagation. The tools of implementation and synthesis are the DK of Celoxica and the ISE of Xilinx. The targeted components are XCV2000 on Celoxica RC1000 board and XC2V1000 on RC200. Experimental evaluations are presented to demonstrate the validity of the design