| A novel system is proposed to recognize and track single and multiple identical and/or dissimilar targets from real life forward looking infrared (FLIR) imagery using correlation filters, namely, maximum average correlation height (MACH), extended MACH (EMACH), distance classifier correlation filter (DCCF) and polynomial DCCF (PDCCF). The MACH and EMACH filters are used for detection while DCCF and PDCCF filters are utilized for classification. Three different two-step algorithms are developed, based on the combination of the detection and classification filters, called MACH-DCCF, MACH-PDCCF and EMACH-PDCCF algorithms. In the first step, the input scene is correlated with all the detection filters (one for each desired target class) of a given algorithm and the resulting correlation outputs are combined. Then a predefined number of regions of interest (ROI) are selected from the input scene based on the higher correlation peak values. In the second step, a classification filter is applied to these ROIs to identify target types and reject clutters/backgrounds. Moving target tracking is accomplished by applying this technique independently to all incoming image frames. The proposed technique yields robust performance for challenging FLIR imagery in terms of faster and accurate detection and classification as well as tracking of the targets. |
