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15 October 2012 Real-time multiclass object recognition system based on adaptive correlation filtering
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A real-time system for multiclass object recognition is proposed. The system is able to identify and correctly classify several moving targets from an input scene by using a bank of adaptive correlation filters with complex constraints implemented on a graphics processing unit. The bank of filters is synthesized with the help of an iterative algorithm based on complex synthetic discriminant functions. At each iteration, the algorithm optimizes the discrimination capability of each filter in the bank by using all available information about the known patterns to be recognized and unwanted patterns to be rejected such as false objects or a background. Computer simulation results obtained with the proposed system in real and synthetic scenes are presented and discussed in terms of pattern recognition performance and real-time operation speed.
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Viridiana Contreras, Victor H. Diaz-Ramirez, Francisco J. Ramirez-Arias, and Kenia Picos "Real-time multiclass object recognition system based on adaptive correlation filtering", Proc. SPIE 8498, Optics and Photonics for Information Processing VI, 849808 (15 October 2012);

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