Simulating Biological Motion Perception Using a Recurrent Neural Network

May 2004

Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004)

Volume

Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004)

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People have the ability to perceive biological motion under conditions of severely limited visual information. If the information is in the form of a point-light motion sequence of a human walker or jogger, perceptual discontinuities are barely noticeable. This phenomenon can be simulated for a machine perceptual system by using a recurrent artificial neural network. A feedback connection from the output of the hidden layer to the input of the hidden layer provides the network with information about past events that can be used to classify an entire sequence of events. In this case, the discrete events are the x,y-coordinates of the point-light displays during a specific motion sequence. Generalizations about temporal as well as spatial patterns can be made that enable the network to classify an input sequence as being either biological or non-biological in nature.