A fast neural network-based detection and tracking of dim moving targets in FLIR imagery

Usually the targets in forward looking infra-red imagery are dim, slowly moving, and buried under clutter and noise. Detecting and tracking of such targets is a challenging task. Although Artificial Neural Networks (ANNs) have been used to solve this problem, they need a lot of training time. In order to reduce the training time, we propose Principal Component Analysis as a dimension reduction technique. We used an MLP with LM learning algorithm and a RBF Neural Network (RBFNN) with K-means algorithm to cluster the data. Both the ANNs are used in a Neural Adaptive Line Enhancer (NALE) configuration. Extensive computer simulations showed the combination of PCA and ANNs gives satisfactory results with significant reduction in training time.