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 to the identification of items being tracked. To address, this we previously proposed a probabilistic formulation of features that supports easy exchange and integration of new features. This paper provides a deeper specification of the formulation. In particular, we employ the Open Geospatial Consortium (OGC) Observations and Measurements (O&M) standard for the new specification. We use OGC O&M to describe non-parametric distributions of image features with respect to the entity resolution problem within feature-based tracking. An example is presented. 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, will result in significantly better and more sustainable growth in tracker performance.

Date Published: 25 May 2012
PDF: 9 pages
Proc. SPIE 8386, Full Motion Video (FMV) Workflows and Technologies for Intelligence, Surveillance, and Reconnaissance (ISR) and Situational Awareness, 83860M (25 May 2012); doi: 10.1117/12.919937

Published in SPIE Proceedings Vol. 8386:
Full Motion Video (FMV) Workflows and Technologies for Intelligence, Surveillance, and Reconnaissance (ISR) and Situational Awareness
Donnie Self, Editor(s)