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Christchurch, New Zealand
Jan. 29, 2002 to Jan. 31, 2002
ISBN: 0-7695-1453-7
pp: 313
Marco Krips , University of Wuppertal
Thomas Lammert , University of Wuppertal
Anton Kummert , University of Wuppertal
ABSTRACT
The advantage of parallel computing of artificial neural networks can be combined with the potentials of VLSI circuits in order to design a real time detection and tracking system applied to video images. Based on these facts, a real-time localization and tracking algorithm has been developed for detecting human hands in video images. Due to the real time aspect, a single-pixel-based classification is aspired, so that a continuous data stream can be processed. Consequently, no storage of full images or parts of them is necessary. The classification, whether a pixel belongs to a hand or to the background, is done by analyzing the RGB-values of a single pixel by means of an artificial neural network. To obtain the full processing speed of this neural network a hardware solution is realized in a Field Programmable Gate Array (FPGA).
INDEX TERMS
artifical neural network, real-time image processing, VLSI design
CITATION
Marco Krips, Thomas Lammert, Anton Kummert, "FPGA Implementation of a Neural Network for a Real-Time Hand Tracking System", DELTA, 2002, Proceedings First IEEE International Workshop on Electronic Design, Test and Applications '2002, Proceedings First IEEE International Workshop on Electronic Design, Test and Applications '2002 2002, pp. 313, doi:10.1109/DELTA.2002.994637
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