Publications

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Vehicle classification is a challenging problem, since vehicles can take on many different appearances and sizes due to their form and function, and the viewing conditions. The low resolution of uncooled-infrared video and the large variability of naturally occurring environmental conditions can make this an even more difficult problem. We develop a multilook fusion approach for improving the performance of a single look system. Our single look approach is based on extracting a signature consisting of a histogram of gradient orientations from a set of regions covering the moving object. We use the multinomial pattern matching algorithm to match the signature to a database of learned signatures. To combine the match scores of multiple signatures from a single tracked object, we use the sequential probability ratio test. Using real infrared data we show excellent classification performance, with low expected error rates, when using at least 25 looks. © 2006 IEEE.

An unattended ground sensor (UGS) that attempts to perform target identification without providing some corresponding estimate of confidence level is of limited utility. In this context, a confidence level is a measure of probability that the detected vehicle is of a particular target class. Many identification methods attempt to match features of a detected vehicle to each of a set of target templates. Each template is formed empirically from features collected from vehicles known to be members of the particular target class. The nontarget class is inherent in this formulation and must be addressed in providing a confidence level. Often, it is difficult to adequately characterize the nontarget class empirically by feature collection, so assumptions must be made about the nontarget class. An analyst tasked with deciding how to use the confidence level of the classifier decision should have an accurate understanding of the meaning of the confidence level given. This paper compares several definitions of confidence level by considering the assumptions that are made in each, how these assumptions affect the meaning, and giving examples of implementing them in a practical acoustic UGS.

The sequential probability ratio test (SPRT) minimizes the expected number of observations to a decision and can solve problems in sequential pattern recognition. Some problems have dependencies between the observations, and Markov chains can model dependencies where the state occupancy probability is geometric. For a non-geometric process we show how to use the effective amount of independent information to modify the decision process, so that we can account for the remaining dependencies. Along with dependencies between observations, a successful system needs to handle the unknown class in unconstrained environments. For example, in an acoustic pattern recognition problem any sound source not belonging to the target set is in the unknown class. We show how to incorporate goodness of fit (GOF) classifiers into the Markov SPRT, and determine the worse case nontarget model. We also develop a multiclass Markov SPRT using the GOF concept.

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