Computationally efficient, real-time motion recognition based on bio-inspired visual and cognitive processing

We propose a novel method for identifying and classifying motions that offers significantly reduced computational cost as compared to deep convolutional neural network systems with comparable performance. Our new approach is inspired by the information processing network architecture of biological visual processing systems, whereby spatial pyramid kernel features are efficiently extracted in real-time from temporally-differentiated image data. In this paper, we describe this new method and evaluate its performance with a hand motion gesture recognition task.

[1]  Gail A Carpenter,et al.  Self-organizing ARTMAP rule discovery , 2012, Neural Networks.

[2]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[4]  Tobi Delbrück,et al.  Touchless hand gesture UI with instantaneous responses , 2012, 2012 19th IEEE International Conference on Image Processing.

[5]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[6]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[7]  Stephen Grossberg,et al.  Gesture recognition system based on Adaptive Resonance Theory , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).