Object tracking by an optoelectronic inner product complex neural network

A complex associative memory model based on a neural network architecture is proposed for tracking three-dimensional objects in a dynamic environment. The storage representation of the complex associative memory model is based on an efficient amplitude-modulated phase-only matched filter. The input to the memory is derived from the discrete Fourier transform of the edge coordinates of the to-be-recognized moving object, where the edges are obtained through motion-based segmentation of the image scene. An adaptive threshold is used during the decision-making process to indicate a match or identify a mismatch. Computer simulation on real-world data proves the effectiveness of the proposed model. The proposed scheme is readily amenable to optoelectronic implementation.