This paper presents initial object profile classification results using range and elevation independent features from a simulated infrared profiling sensor. The passive infrared profiling sensor was simulated using a LWIR camera. A field data collection effort to yield profiles of humans and animals is reported. Range and elevation independent features based on height and width of the objects were extracted from profiles. The profile features were then used to train and test four classification algorithms to classify objects as humans or animals. The performance of Naïve Bayesian (NB), Naïve Bayesian with Linear Discriminant Analysis (LDA+NB), K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) are compared based on their classification accuracy. Results indicate that for our data set SVM and (LDA+NB) are capable of providing classification rates as high as 98.5%. For perimeter security applications where misclassification of humans as animals (true negatives) needs to be avoided, SVM and NB provide true negative rates of 0% while maintaining overall classification rates of over 95%.
This paper describes the development of linear pyroelectric array systems for classification of human, animal, and vehicle targets. The pyroelectric array is simulated to produce binary profiles of targets. The profiles are classified based on height to width ratio using Naïve Bayesian classifiers. Profile widths of targets can vary due to the speed of the target. Target speeds were calculated using two techniques; two array columns, and a tilted array. The profile width was modified by the calculated speeds to show an improvement in classification results.
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