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Learning Vehicle Traffic Videos using Small-World Attractor Neural Networks

The goal of this work is to learn and retrieve a sequence of highly correlated patterns using a Hopfield-type of Attractor Neural Network (ANN) with a small-world connectivity distribution. For this model, we propose a weight learning heuristic which combines the pseudo-inverse approach with a row-shifting schema. The influence of the ratio of random connectivity on retrieval quality and learning time has been studied. Our approach has been successfully tested on a complex pattern, as it is the case of traffic video sequences, for different combinations of the involved parameters. Moreover, it has demonstrated to be robust with respect to highly-variable frame activity.