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The classification of motion in image sequences using 3D recursive adaptive filters to obtain neural network input vectors

A new application of neural networks is described that permits the selective classification of objects on the basis of their motion in digital image sequences. An adaptive 3D recursive linear-trajectory (LT) filter is employed to track a moving object on a smoothly-varying space-time trajectory. The trajectory information produced by the adaptive 3D LT filter is used as the input vector to a conventional multilayer perceptron (MLP) neural network to perform the classification of motion.