Automatic target recognition of multiple targets from two classes with varying velocities using correlation filters

Abstract

Correlation filters (CFs) can detect multiple targets in one scene making them well-suited for automatic target recognition (ATR) applications. We present a method to efficiently compute the Quadratic CF (QCF) capable of detecting multiple targets from two classes. We use a Kalman filter framework to combine information from successive correlation outputs in a probabilistic way integrating the ATR tasks of detection, recognition, and tracking.