Title :
The classification of motion in image sequences using 3D recursive adaptive filters to obtain neural network input vectors
Author :
Bruton, L.T. ; Bartley, N.R. ; Liu, Z.Q.
Author_Institution :
Dept. of Electr. & Comput. Eng., Calgary Univ., Alta., Canada
Abstract :
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
Keywords :
adaptive filters; filtering theory; image classification; image sequences; motion estimation; multilayer perceptrons; recursive filters; 3D recursive adaptive filters; adaptive 3D recursive linear-trajectory filter; digital image sequences; image sequences; motion classification; multilayer perceptron neural network; neural network input vectors; selective classification; smoothly-varying space-time trajectory; Adaptive filters; Digital images; Image sequences; Neural networks; Noise robustness; Nonlinear filters; Passband; Tracking; Trajectory; Vehicles;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.488856
