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FPGA implementation of a neural network for a real-time hand tracking system

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3 Author(s)
Krips, M. ; Dept. of Electr. & Inf. Eng., Wuppertal Univ., Germany ; Lammert, T. ; Kummert, A.

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)

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Electronic Design, Test and Applications, 2002. Proceedings. The First IEEE International Workshop on

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