Automated motion image-based tracking is an increasingly important tool in Intelligence, Surveillance, and Reconnaissance (ISR). Unfortunately, current tracking technology is not up to the performance levels needed to deliver key subtasks in this arena. We postulate that the under-performance of automated trackers derives from the under-exploitation of the rich sets of features related the identification of items being tracked. To address this we propose a formulation of features that supports easy exchange and integration of new features. We believe this approach will provide the foundations for a far wider and more effective exploration of potential features related to tracking, and as a result, significantly better and more sustainable growth in tracker performance
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