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 ord...

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Bibliographic Details
Main Authors: Patra, J.C., Widjaja, F., Das, A., Ee Luang Ang
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Clustering algorithms
Intelligent networks
Line enhancers
Neural networks
Object detection
Principal component analysis
Real time systems
Sonar
Target tracking
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Summary: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.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2005.1556430